Method and device for generating object combination, electronic equipment and readable medium

Abstract

The invention discloses a method and device for generating an object combination, electronic equipment and a readable medium, in the method, reference combination object information is acquired, and the reference combination object information comprises object attribute information corresponding to a plurality of reference objects respectively; for each reference object, a target candidate object corresponding to the reference object is determined from the candidate object set according to the object attribute information corresponding to the reference object, and the matching degree of the object attribute information of the target candidate object and the object attribute information corresponding to the reference object meets a preset condition; and taking the target candidate objects respectively corresponding to the plurality of reference objects as an object combination. Thus, manual object matching by workers is not needed, the purpose of automatically generating the object combination can be achieved through the automatic matching function integrated in the background of the management platform, convenience is brought to the workers, and the quality of the automatically generated object combination can also be guaranteed.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, electronic device and readable medium for generating an object combination. Background Art

[0002] With the development of Internet technology, people can operate online objects through online behaviors, and staff can also better display objects to people through editing, management, etc. For example, online store staff can display the goods and / or services in the online store through editing, management, etc., and users can browse, collect or purchase the goods and / or services in the online store.

[0003] Currently, staff can manually combine multiple objects on the line into an object combination on an online management platform (hereinafter referred to as the management platform), and display the object combination to people, who can browse, view and even purchase the object combination online.

[0004] However, the manual matching of object combinations by the staff is not convenient enough for the staff, and the matched object combinations are also of varying quality. The purpose of optimizing the online marketing situation cannot be achieved by matching object combinations. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a method, device, electronic device and readable medium for generating object combinations, which can automatically and intelligently generate object combinations, which not only facilitates the staff, but also ensures the quality of the automatically generated object combinations, thereby making it possible to optimize online marketing situations.

[0006] In order to achieve the above objectives, the technical solutions provided by this application are as follows:

[0007] In a first aspect, the present application provides a method for generating an object combination, comprising:

[0008] Acquire reference combination object information, wherein the reference combination object information includes object attribute information corresponding to each of a plurality of reference objects;

[0009] For each reference object, determining a target candidate object corresponding to the reference object from a candidate object set according to the object attribute information corresponding to the reference object; wherein the degree of matching between the object attribute information of the target candidate object and the object attribute information corresponding to the reference object satisfies a preset condition;

[0010] The target candidate objects respectively corresponding to the multiple reference objects are taken as an object combination.

[0011] In a possible implementation manner, obtaining the reference combination object information includes:

[0012] From the collocation main image, identifying a plurality of reference objects included in the collocation main image;

[0013] According to the sub-picture corresponding to each reference object among the multiple reference objects in the matching main picture, object attribute information of the corresponding reference object is obtained.

[0014] As an example, obtaining object attribute information of a corresponding reference object according to a sub-picture corresponding to each reference object in the matching main picture includes:

[0015] The sub-image corresponding to each reference object among the multiple reference objects in the matching main image is input into the feature detection model to obtain object attribute information of the corresponding reference object.

[0016] In a possible implementation manner, determining, from a candidate object set, a target candidate object corresponding to the reference object according to the object attribute information corresponding to the reference object, includes:

[0017] From a preset retrieval library, object attribute information whose matching degree with the object attribute information corresponding to the reference object satisfies the preset conditions is determined, and the candidate object corresponding to the determined object attribute information is recorded as the target candidate object, the retrieval library includes a mapping relationship between each candidate object in a plurality of candidate objects and the corresponding object attribute information, and the candidate object set includes the plurality of candidate objects involved in the retrieval library.

[0018] In a possible implementation, the process of constructing the preset search library includes:

[0019] Obtaining a target image for each candidate object, wherein the target image includes only one candidate object;

[0020] Based on the feature detection model, obtaining object attribute information corresponding to each of the target images;

[0021] The mapping relationship between each candidate object and the corresponding object attribute information is saved accordingly to obtain the search library.

[0022] In a possible implementation, the training process of the feature detection model includes:

[0023] Based on the pictures in the training samples, the initial model is pre-trained in a self-supervised manner to obtain a first model;

[0024] Based on the pictures in the training samples and the classification labels corresponding to the pictures, the first model is trained in one stage by clustering to obtain a second model;

[0025] Based on the two images in the training sample and the sample attributes of the two images, the second model is trained in two stages at least once using a binary classification method, and the second model obtained from the last two-stage training is recorded as the feature detection model.

[0026] In a possible implementation, the object combination includes a first combination of reference objects included in the collocation main image, and the method further includes:

[0027] Corresponding anchor points are added at preset positions of the areas where the reference objects in the matching main image are located, respectively, to obtain the combined image of the first combination, and the anchor points are used to associate with preset attribute information of the corresponding reference objects.

[0028] In a second aspect, the present application also provides a device for generating an object combination, including:

[0029] An acquisition unit, configured to acquire reference combination object information; wherein the reference combination object information includes object attribute information corresponding to each of a plurality of reference objects;

[0030] A matching unit, configured to determine, for each reference object, a target candidate object corresponding to the reference object from a candidate object set according to the object attribute information corresponding to the reference object; wherein the degree of matching between the object attribute information of the target candidate object and the object attribute information corresponding to the reference object satisfies a preset condition;

[0031] The generating unit is used to combine the target candidate objects corresponding to each of the plurality of reference objects into an object combination.

[0032] In a possible implementation, the acquiring unit includes:

[0033] An identification subunit, used to identify a plurality of reference objects included in the matching main picture from the matching main picture;

[0034] The first obtaining subunit is used to obtain object attribute information of a corresponding reference object according to a sub-picture corresponding to each reference object among the multiple reference objects in the matching main picture.

[0035] As an example, the first obtaining subunit is specifically used for:

[0036] The sub-image corresponding to each reference object among the multiple reference objects in the matching main image is input into the feature detection model to obtain object attribute information of the corresponding reference object.

[0037] In a possible implementation, the matching unit is specifically configured to:

[0038] From a preset retrieval library, object attribute information whose matching degree with the object attribute information corresponding to the reference object satisfies the preset conditions is determined, and the candidate object corresponding to the determined object attribute information is recorded as the target candidate object, the retrieval library includes a mapping relationship between each candidate object in a plurality of candidate objects and the corresponding object attribute information, and the candidate object set includes the plurality of candidate objects involved in the retrieval library.

[0039] In a possible implementation, the device further includes: a construction unit;

[0040] The construction unit is used to construct a preset retrieval library.

[0041] As an example, the construction unit includes:

[0042] A second obtaining subunit is used to obtain a target image of each candidate object, wherein the target image includes only one candidate object;

[0043] A third obtaining subunit, configured to obtain object attribute information corresponding to each of the target images based on the feature detection model;

[0044] The saving subunit is used to save the mapping relationship between each candidate object and the corresponding object attribute information to obtain the search library.

[0045] In a possible implementation, the device further includes: a training unit;

[0046] The training unit is used to train the feature detection model.

[0047] As an example, the training unit includes:

[0048] A first training subunit is used to pre-train the initial model in a self-supervised manner based on the pictures in the training sample to obtain a first model;

[0049] A second training subunit is used to perform a one-stage training on the first model in a clustering manner based on the pictures in the training samples and the classification labels corresponding to the pictures to obtain a second model;

[0050] The third training subunit is used to perform at least one two-stage training on the second model using a binary classification method based on the two pictures in the training samples and the sample attributes of the two pictures, and record the second model obtained from the last two-stage training as the feature detection model.

[0051] In a possible implementation, the object combination includes a first combination of reference objects included in the collocation main image, and the device further includes: an anchor unit;

[0052] The anchor point unit is used to add corresponding anchor points at preset positions of the areas where the reference objects in the matching main image are located, so as to obtain the combined image of the first combination, and the anchor points are used to associate with preset attribute information of the corresponding reference objects.

[0053] It should be noted that the specific implementation method of the device and the technical effects achieved can refer to the relevant description of the method provided in the first aspect or any implementation method of the first aspect.

[0054] In a third aspect, the present application further provides an electronic device, the electronic device comprising: a processor and a memory;

[0055] The memory is used to store instructions or programs;

[0056] The processor is used to execute the instructions or programs in the memory so that the electronic device executes the method provided by the first aspect or any one of the implementations of the first aspect.

[0057] In a fourth aspect, the present application further provides a readable medium, in which instructions or programs are stored. When the instructions or programs are executed on a processor, the processor executes the method provided in the first aspect or any one of the implementations of the first aspect.

[0058] Compared with the prior art, the embodiments of the present application have at least the following advantages:

[0059] In the technical solution provided by the present application, an automatic matching function is provided online. After the staff executes the function, the reference combination object information can be obtained, and the reference combination object information includes the object attribute information corresponding to each of the multiple reference objects; for each reference object, according to the object attribute information corresponding to the reference object, the target candidate object corresponding to the reference object is determined from the candidate object set, wherein the matching degree of the object attribute information of the target candidate object and the object attribute information corresponding to the reference object meets the preset conditions; then, the target candidate objects corresponding to each of the multiple reference objects are taken as an object combination. In this way, there is no need for the staff to manually match the objects. Due to the automatic matching function integrated in the background of the management platform, the staff only needs to use the automatic matching function, and the management platform can use the multiple reference objects included in the known matching as the basis for matching, and match at least one target candidate object corresponding to each reference object based on the object attribute information of the reference object and the object attribute information of the candidate object in the candidate object set, so as to generate the combination of the target candidate objects corresponding to different reference objects as an object combination, and realize the purpose of automatically generating the object combination, which is not only convenient for the staff, but also because the known matching of the reference object is taken as the basis, the quality of the automatically generated object combination can also be guaranteed, thereby making it possible to optimize the online marketing situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A flowchart of a method for generating an object combination provided in an embodiment of the present application;

[0062] Figure 2 A schematic diagram of an object detection provided in an embodiment of the present application;

[0063] Figure 3 A schematic diagram of a feature detection model provided in an embodiment of the present application;

[0064] Figure 4 A schematic diagram of a training process of a feature detection model provided in an embodiment of the present application;

[0065] Figure 5 A schematic diagram of a process for generating an object combination based on a graph recognition algorithm provided in an embodiment of the present application;

[0066] Figure 6A schematic diagram of another process of generating object combinations based on a graph recognition algorithm provided in an embodiment of the present application;

[0067] Figure 7 A schematic diagram of determining an object combination anchor point in a matching main image provided by an embodiment of the present application;

[0068] Figure 8 A schematic diagram of the structure of a device 800 for generating an object combination provided in an embodiment of the present application;

[0069] Fig. 9 A schematic diagram of the structure of an electronic device 900 provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] In some cases, users want to view and purchase multiple objects with specific relationships through online entities. For the convenience of users, online entities usually display multiple objects next to each other or as a combination of objects. The scenario where an online entity displays multiple objects as a combination of objects to users can be understood as the online entity performing a matching operation on multiple objects. For example, an online store can combine nursing pads, confinement caps, confinement shoes and other products in a maternity bag. For another example, an online store can combine shirts and trousers in a commuting suit combination.

[0071] At present, the staff of online entities usually manually enter the matched object combinations on the operation platform of the online entity according to their own experience or the sales data of each object. For example, the operation platform of an online store has a "product matching" function. The staff enters the editing page through "new matching" and creates a matching combination on the editing page by uploading the matching main picture, adding products, etc. After the matching combination passes the review, it can be released and put on the shelf for users to view and / or purchase. Although this process can facilitate users to view and / or purchase on the online entity, it is not very friendly to the staff of the online entity. On the one hand, the staff needs to perform complex editing and other operations on the objects they want to match. On the other hand, the quality of the object combination generated by the manual object matching of the staff cannot be guaranteed. In many cases, the object combination provided cannot meet the needs of users. Therefore, the current method of matching objects is time-consuming and labor-intensive and has little effect.

[0072] Based on this, an embodiment of the present application provides a method for generating an object combination. Taking the management platform of an online entity as an example, an automatic matching function is integrated in the background of the management platform, so that the management platform can provide the automatic matching function for the staff of the online entity. Taking the automatic matching demand of the staff of the online entity as an example, the device for generating an object combination can obtain reference combination object information, and the reference combination object information includes object attribute information corresponding to each of a plurality of reference objects; for each reference object, according to the object attribute information corresponding to the reference object, the target candidate object corresponding to the reference object is determined from the candidate object set, wherein the degree of matching between the object attribute information of the target candidate object and the object attribute information corresponding to the reference object meets the preset conditions; then, the target candidate objects corresponding to each of the plurality of reference objects are taken as an object combination.

[0073] In this way, there is no need for staff to manually match objects. Due to the automatic matching function integrated in the background of the management platform, the staff only needs to use the automatic matching function, and the management platform can use the multiple reference objects included in the known matching as the basis for matching, and match at least one target candidate object corresponding to each reference object based on the object attribute information of the reference object and the object attribute information of the candidate object in the candidate object set, so as to generate the combination of target candidate objects corresponding to different reference objects as an object combination, thereby achieving the purpose of automatically generating object combinations. This not only facilitates the staff, but also because the known matching of reference objects is used as a benchmark, the quality of the automatically generated object combination can also be guaranteed, thereby making it possible to optimize online marketing situations.

[0074] It should be noted that the management platform of the online entity can be: a management platform page that the online entity can operate after logging in to the management platform provided by the e-commerce enterprise that hosts the online entity. The management platform can be in the form of a client or a web page.

[0075] It should be noted that the subject implementing the method of generating an object combination may be the device for generating an object combination provided in the embodiment of the present application. The device for generating an object combination may be carried in an electronic device or a functional module of an electronic device. Taking the scenario of the management platform of an online entity as an example, the device for generating an object combination may include the management platform of an online entity, or the device for generating an object combination may be connected to the management platform of an online entity; the electronic device may be installed with a client corresponding to the management platform of the online entity, or may access a webpage corresponding to the management platform of the online entity.

[0076] Figure 1 The present invention provides a method for generating an object combination according to an embodiment of the present invention. The method can be applied to a device for generating an object combination. The device for generating an object combination can be, for example, Figure 8The device 800 for generating an object combination is shown. Corresponding to the scenario of the online entity management platform, the device for generating an object combination may include the online entity management platform or be connected to the online entity management platform, so the method may also be considered to be applied to the online entity management platform.

[0077] like Figure 1 As shown, the method may include, for example, the following S101 to S103:

[0078] S101, obtaining reference combination object information, wherein the reference combination object information includes object attribute information corresponding to each of a plurality of reference objects.

[0079] In the embodiment of the present application, the management platform of the online entity has an operation item corresponding to the automatic matching function. By the staff's action on the operation item, it can be considered that the target operation has been performed on the management platform. The execution of the target operation triggers the background of the management platform to automatically match objects for the target entity corresponding to the staff. For example, the interface corresponding to the management platform of the online entity may include a "smart matching recommendation" button, and the "smart matching recommendation" button can correspond to the operation item corresponding to the automatic matching function on the management platform of the online entity. When the user clicks the "smart matching recommendation" button, it is equivalent to performing the target operation on the management platform, thereby triggering the management platform to execute the method.

[0080] In the process of automatically matching objects for the target entity in the background of the management platform, it is necessary to first obtain the matching information representing the known matching in the target entity, and use the objects included in the matching information as reference objects to implement the method provided in the embodiment of the present application, that is, the matching information can be understood as the data basis for generating object combinations in the embodiment of the present application. Matching information can refer to any information that can indicate a known match, which can be text description information or a picture. For example, the matching information can be a plurality of pictures corresponding to each object in the target entity, which includes at least one picture of other objects in the target entity in addition to the object, that is, the matching main picture referred to below. The multiple pictures corresponding to the object may include, for example, the header picture, detail picture or carousel picture of the object. In order to display more information, the detail picture or carousel picture of the object may include other objects in addition to the object itself. Therefore, the detail picture or carousel picture including multiple objects can be used as the matching main picture.

[0081] As an example, S101 may include: S101a, identifying multiple reference objects included in the matching main picture from the matching main picture; S101b, obtaining object attribute information of the corresponding reference object according to the sub-picture corresponding to each reference object in the matching main picture. Wherein, S101b may include: inputting the sub-picture corresponding to each reference object in the matching main picture among the multiple reference objects into a feature detection model to obtain object attribute information of the corresponding reference object. Wherein, the object attribute information may, for example, represent any information of the object attribute, for example, may include information representing the style, color, category and other attributes of the object. The object attribute information may be represented by the feature vector of the object.

[0082] The implementation process of S101a may include, for example: S101a1, performing object detection (also referred to as subject detection) on the matching main image, and determining that the matching main image includes the first object and at least one other object; S101a2, cropping the objects detected in the matching main image, and cropping a first picture including the first object and a second picture including each other object from the matching main image. Both the first picture and the second picture are partial pictures in the matching main image, and the first picture only includes the first object and a part of the background of the first object in the matching main image, and the partial background does not include the content of other objects as much as possible. Similarly, the second picture only includes one other object and a part of the background of the other object in the matching main image, and the partial background does not include the content of any object other than the other object as much as possible. Among them, the first object and the at least one other object are both reference objects in S101.

[0083] For example, the matching main image obtained from the image of the first object of the target entity can be found in Figure 2 In the picture 1, the first object is a top A, and the matching main picture also includes a skirt B (i.e., other objects). Through S101a, the first picture and the second picture can be obtained, wherein the first picture corresponds to Figure 2 In picture 2, the second picture can correspond to Figure 2 In Picture 3, Picture 2 is the part corresponding to top A in Picture 1, and Picture 3 is the part corresponding to skirt B in Picture 1.

[0084] The implementation process of S101b may include, for example: inputting the first picture into a feature detection model, the feature detection model outputting a first feature vector; similarly, inputting each second picture in at least one second picture into a feature detection model, the feature detection model outputting a second feature vector corresponding to the input second picture. The feature detection model may also be referred to as a picture encoder according to its function, the input of the feature detection model is a picture, the output is a feature vector of the picture, and the output feature vector can characterize the properties of the object included in the input picture (also referred to as a subject feature).

[0085] For example, the feature detection model can be a Swin Transformer. The overall architecture of the Swin Transformer can be found in Figure 3 As shown in the figure. Swin Transformer adopts a hierarchical design, which consists of 4 parts (Stage). Each stage will reduce the resolution of the input image and expand the receptive field layer by layer like Convolutional Neural Networks (CNN). Figure 3 As shown on the left, at the beginning of the Swin Transformer input, a patch embedding is performed to cut the image into tiles and embed them into the Embedding. In each Stage, it consists of a patch merging module and at least one block. The Patch Merging module downsamples the image data entering each Stage to reduce the image resolution. The specific structure of the Block can be seen in Figure 3 As shown on the right, it mainly includes a structure composed of layer normalization (LayerNormalization, LN), multi-layer perceptron (Multi-Layer Perceptron, MLP), window attention (Window Attention) and shifted window attention (ShiftedWindow Attention). Swin Transformer, as a model including sliding window operation and hierarchical design, is used as a feature detection model in the embodiment of the present application. Its sliding window operation includes non-overlapping local windows (local window) and overlapping cross-windows (cross-window), which limits the attention calculation to one window. On the one hand, it can introduce the locality of CNN convolution operation, and on the other hand, it can save the amount of calculation.

[0086] In order to better obtain the feature vector of the picture, it is necessary to train the feature detection model to a model with better feature detection performance. The embodiment of the present application provides a method for multi-stage training of the feature detection model. For example, the training of the feature detection model may include: S11, based on the pictures in the training sample, the initial model is pre-trained in a self-supervised manner to obtain a first model; S12, based on the pictures in the training sample and the classification labels corresponding to the pictures, the first model is trained in a clustering manner in one stage to obtain a second model; S13, based on the two pictures in the training sample and the sample attributes of the two pictures, the second model is trained in a two-stage manner at least once, and the second model obtained by the last two-stage training is recorded as the feature detection model.

[0087] See also Figure 4 As shown, for the pre-training in S11, it can be implemented through multimodal pre-training models such as Contrastive Language-Image Pre-training (CLIP), Bootstraping Language-Image Pre-training (BLIP), BEiT2 and BEiT3. For the one-stage training of S12, it can be implemented by spectral clustering of subcenter, arcface, partial-fc, id classification, infonce and triplet. For the two-stage training in S13, for example, it can include: constructing correlation classification data + rule difficult sample mining, which is implemented by classification, main or whole image fusion, or smoothL1; for example, it can include: manually labeling difficult samples, which is implemented by binary classification, Triplet or InfoNce, or continuous mining of difficult samples.

[0088] Matching the main picture Figure 2 Taking the picture 1 as an example, the reference combination object information obtained in S101 may include: feature vector 2 corresponding to picture 2 and feature vector 3 corresponding to picture 3. The top A in picture 2 and the skirt B in picture 3 are two reference objects corresponding to the reference combination object information.

[0089] S102, for each reference object, determining a target candidate object corresponding to the reference object from a candidate object set according to object attribute information corresponding to the reference object, wherein the degree of matching between the object attribute information of the target candidate object and the object attribute information corresponding to the reference object satisfies a preset condition.

[0090] It should be noted that, for each of the multiple reference objects corresponding to the reference combination object information obtained in S101, at least one target candidate object corresponding to the reference object can be obtained based on S102. For example, the two reference objects corresponding to the reference combination object information obtained in S101 are Figure 2 For the top A in picture 2 and the skirt B in picture 3, then, for the top A, execute S102 to obtain at least one target candidate object 1 corresponding to the top A; for the skirt B, execute S102 to obtain at least one target candidate object 2 corresponding to the skirt B.

[0091] As an example, for each reference object, S102 may include: determining, from a preset search library, object attribute information whose matching degree with the object attribute information corresponding to the reference object satisfies the preset condition, and recording the candidate object corresponding to the determined object attribute information as the target candidate object, the search library including a mapping relationship between each candidate object in a plurality of candidate objects and the corresponding object attribute information, and the candidate object set including the plurality of candidate objects involved in the search library. In the following, the object attribute information is described by the feature vector of the object.

[0092] The retrieval library may be a database that is pre-built and maintained before executing S102 and includes mapping relationships between objects and their feature vectors. The set of objects included in the retrieval library is the candidate object set in S102. The retrieval library is used to provide a data basis for retrieving target candidate objects that match the reference objects, so as to determine target candidate objects that have the same and / or similar attributes in the target entity as the multiple reference objects included in the matching main graph.

[0093] In some possible implementations, before S102, the method may further include a process of constructing a search library. The process of constructing a search library may include, for example: S100a, obtaining a target image of each candidate object, wherein the target image includes only one candidate object; S100b, obtaining object attribute information corresponding to each target image based on the feature detection model; S100c, saving the mapping relationship between each candidate object and the corresponding object attribute information, and obtaining the search library. Among them, the target image of each candidate object is an image that only includes the candidate object but not other objects, such as a stock keeping unit (SKU) image. The SKU image refers to an image that displays the attributes that can distinguish the object. For example, for each commodity, its brand, trademark, color, model and other attributes are different from other commodities, so the SKU image is an image that displays the above attributes of the commodity. In this embodiment, the SUK image is only exemplary, and any image that includes the object itself but not other objects can also be used in S100a to replace the SKU image of the object. The feature detection model in S100b can be the same as the feature detection model for implementing S101b, both of which are trained models for extracting feature vectors of images. After S100c, the detection library may include, for example: candidate object 1-feature vector 1, candidate object 2-feature vector 2, ..., candidate object n-feature vector n, n is an integer greater than or equal to 1, and the candidate object set may include: candidate object 1, candidate object 2, ..., candidate object n. It should be noted that the content in the retrieval library can be continuously updated based on the increase, decrease or change of the objects included in the target entity. For example, when the objects included in the target entity change, the embodiment of the present application can execute S100a~S100c on the candidate objects involved in the change, thereby obtaining an updated retrieval library, so that the staff of the target entity can obtain the most accurate automatic matching results at any time.

[0094] Among them, the degree of matching between the object attribute information of the target candidate object and the object attribute information corresponding to the reference object meets the preset conditions, which can also be understood as the match between the object attribute information of the target candidate object and the object attribute information of the reference object, which can specifically mean that the object attribute information of the target candidate object is the same or similar to the object attribute information of the reference object, and the similarity between the object attribute information of the target candidate object and the object attribute information of the reference object can mean that the difference between the object attribute information of the target candidate object and the object attribute information of the reference object is within a preset range.

[0095] S103: taking the target candidate objects corresponding to each of the plurality of reference objects as an object combination.

[0096] As an example, S103 may include: arbitrarily combining and matching each reference object among multiple reference objects with the target candidate objects corresponding to other reference objects based on the target candidate object obtained in S102 to obtain multiple object combinations, each object combination including a target candidate object corresponding to each reference object in at least two reference objects among the multiple reference objects.

[0097] Still Figure 2 Taking the shown matching main picture as an example, the reference objects include top A and skirt B. If for top A, S102 is executed to obtain target candidate objects 1 corresponding to top A including {top A1, top A2 and top A3}; if for skirt B, S102 is executed to obtain at least one target candidate object 2 corresponding to skirt B including {skirt B1 and skirt B2}; then, the object combinations obtained based on S103 may include: object combination 1 {top A1, skirt B1}, object combination 2 {top A1, skirt B2}, object combination 3 {top A2, skirt B1}, object combination 4 {top A2, skirt B2}, object combination 5 {top A3, skirt B1} and object combination 6 {top A3, skirt B2}. Optionally, the above six object combinations may include the combination of {top A, skirt B}, that is, one of the tops A1, A2, and A3 is top A itself, and one of the skirts B1 and B2 is skirt B itself. Alternatively, the above six object combinations do not include the combination of {top A, skirt B}, then the object combination obtained in S103 may also include {top A, skirt B}.

[0098] In some other implementations, if the reference combination object information corresponds to more than two reference objects, then an object combination may be generated based on the target candidate objects corresponding to at least two reference objects. Assuming that the reference objects corresponding to the reference combination object information include A, B, and C, if for A, executing S102 obtains target candidate object 1 corresponding to A including {A1, A2, and A3}; if for B, executing S102 obtains at least one target candidate object 2 corresponding to B including {B1 and B2}; if for C, executing S102 obtains at least one target candidate object 3 corresponding to C including {C1}, then the object combination obtained based on S103 may include: object combination 1 {A1, B1}, object combination 2 {A1, B2}, object combination 3 {A2, B1}, object combination 4{A2, B2}, object combination 5{A3, B1}, object combination 6{A3, B2}, object combination 7{A1, C1}, object combination 8{A2, C1}, object combination 9{A3, C1}, object combination 10{B1, C1}, object combination 11{B2, C1}, object combination 12{A1, B1, C1}, object combination 13{A1, B2, C1}, object combination 14{A2, B1, C1}, object combination 15{A2, B2, C1}, object combination 16{A3, B1, C1} and object combination 17{A3, B2, C1}.

[0099] The method provided in the embodiments of the present application can be found, for example, Figure 5 As shown, it includes 100a to S100c for constructing a search library and S101a1, S101a2, S101b, S102 to S103 for automatic matching. Figure 2 Take the picture 1 shown as an example, the automatic matching process can be seen in Figure 6As shown, it includes: S21, performing object detection and cropping from picture 1 to obtain picture 2 and picture 3; S22, inputting pictures 2 and 3 into a feature detection model, and the feature detection model outputs a feature vector 2 corresponding to picture 2 and a feature vector 3 corresponding to picture 3; S23, searching the feature vector 2 in a retrieval library to obtain a target candidate object 1, wherein the target candidate object 1 includes at least a top A, searching the feature vector 3 in a retrieval library to obtain a target candidate object 2, wherein the target candidate object 2 includes at least a skirt B, and the retrieval library includes a correspondence 1 between the feature vector 2' of each target candidate object 1 and the corresponding target candidate object 1, and a correspondence 2 between the feature vector 3' of each target candidate object 2 and the corresponding target candidate object 2; S24, determining at least one object combination in the target entity, wherein the at least one object combination includes a first combination: top A and skirt B. Among them, the degree of matching between feature vector 2 and feature vector 2' satisfies the preset conditions, for example, it can mean that feature vector 2 is the same or similar to feature vector 2', and the similarity between feature vector 2 and feature vector 2' can mean that the difference between feature vector 2 and feature vector 2' is within a preset range; similarly, feature vector 3 and feature vector 3' match, for example, it can mean that feature vector 3 is the same or similar to feature vector 3', and the similarity between feature vector 3 and feature vector 3' can mean that the difference between feature vector 3 and feature vector 3' is within a preset range.

[0100] In some possible implementations, the object combination obtained in S103 may include a first combination of reference objects included in the matching main image, such as {top A, skirt B} in the above example. Then, after S103, the method may further include: adding corresponding anchor points at preset positions of the area where each reference object in the matching main image is located, respectively, to obtain a combined image of the first combination, wherein the anchor points are used to associate preset attribute information of the corresponding reference object. The preset position may be, for example, the position [0.6, 0.7] of the area where the object is located. An anchor point refers to a mark that can link to the object details page of an object. For example, when there is no operation on a certain anchor point, the anchor point can be displayed as a dot; when the user moves the cursor to the preset area where the anchor point is located, the preset attribute information associated with the anchor point can be, for example, certain key information of the corresponding reference object (such as product name, price, etc.), and the certain key information of the reference object corresponding to the anchor point is displayed near the anchor point; when the user clicks on the anchor point, the preset attribute information associated with the anchor point can be, for example, the Uniform Resource Locator (URL) of the object details page of the corresponding reference object, and the page jumps to the object details page corresponding to the URL. Optionally, other relevant information of the reference object can also be added in the direction corresponding to each reference object in the first combination, such as at least one of the name of the object, the category to which it belongs, the header image, or the SKU image. For Figure 2 The matching main picture shown in FIG. 1 shows that the first combination of the object combinations obtained based on this method is {top A, skirt B}. Then, the combination picture of the first object obtained in this embodiment can be referred to as Figure 7 As shown. In this way, the object images in the object database of the target entity in the online platform are first captured, and the matching main picture including multiple objects is screened. For the captured images including the objects on the matching main picture, the objects published by the target entity are matched, and the positions of the determined objects are marked on the matching main picture, so as to intelligently and quickly complete the construction of the object combination.

[0101] It is understandable that after generating at least one object combination of the target entity, the at least one object combination of the target entity can be saved in the form of a file for viewing, editing and other operations by the staff of the target entity.

[0102] In some possible implementations, in order to subsequently optimize the matching algorithm and to provide a better experience for online entities, the method may also include: calculating the association of the object combination based on the page views, sales volume, buyer evaluation, staff evaluation, and sales association of each object in the object combination within the target entity, thereby determining the matching score of the object combination based on the association. Optionally, the matching score can be displayed on the management platform to guide the display and / or release of the object combination. For example, the staff can choose to put the object combination with a higher matching score on the shelf. For another example, the staff can choose to "adjust" the display order of each object combination in the display page of the online entity according to the size of the matching score. The staff can even set the matching score threshold for the display. The object combination corresponding to the matching score below the matching score threshold will not be displayed on the display page. For another example, the staff can also affect the matching score of the object combination by filling in the evaluation.

[0103] It should be noted that, for at least one object combination generated by S103, it can be an object combination that has been deduplicated by the same combination and / or filtered by the same category in the match; or, at least one object combination generated by S103 can be an object combination that has not been deduplicated by the same combination and / or filtered by the same category in the match. Then, after S103, the method can also perform deduplication of the same combination and / or filtering of the same category in the match, so that the object combination finally provided to the staff of the target entity has better quality, so as to improve the user experience of using the automatic matching function.

[0104] It can be seen that in this method, there is no need for staff to manually match objects. Since the automatic matching function is integrated in the background of the management platform, the staff only needs to use the automatic matching function, and the management platform can use the multiple reference objects included in the known matching as the basis for matching, and match at least one target candidate object corresponding to each reference object based on the object attribute information of the reference object and the object attribute information of the candidate object in the candidate object set, so as to generate the combination of target candidate objects corresponding to different reference objects as an object combination, thereby achieving the purpose of automatically generating object combinations. This not only facilitates the staff, but also because the known matching of the reference objects is used as a benchmark, the quality of the automatically generated object combination can also be guaranteed, thereby making it possible to optimize online marketing situations.

[0105] Accordingly, the embodiment of the present application also provides a device 800 for generating an object combination, see Figure 8 As shown, the apparatus 800 may include:

[0106] The acquisition unit 801 is used to acquire reference combination object information; wherein the reference combination object information includes object attribute information corresponding to each of a plurality of reference objects;

[0107] A matching unit 802 is used to determine, for each reference object, a target candidate object corresponding to the reference object from a candidate object set according to the object attribute information corresponding to the reference object; wherein the degree of matching between the object attribute information of the target candidate object and the object attribute information corresponding to the reference object satisfies a preset condition;

[0108] The generating unit 803 is configured to combine the target candidate objects corresponding to each of the plurality of reference objects into an object combination.

[0109] In a possible implementation, the acquiring unit 801 includes:

[0110] An identification subunit, used to identify a plurality of reference objects included in the matching main picture from the matching main picture;

[0111] The first obtaining subunit is used to obtain object attribute information of a corresponding reference object according to a sub-picture corresponding to each reference object among the multiple reference objects in the matching main picture.

[0112] As an example, the first obtaining subunit is specifically used for:

[0113] The sub-image corresponding to each reference object among the multiple reference objects in the matching main image is input into the feature detection model to obtain object attribute information of the corresponding reference object.

[0114] In a possible implementation, the matching unit 802 is specifically configured to:

[0115] From a preset retrieval library, object attribute information whose matching degree with the object attribute information corresponding to the reference object satisfies the preset conditions is determined, and the candidate object corresponding to the determined object attribute information is recorded as the target candidate object, the retrieval library includes a mapping relationship between each candidate object in a plurality of candidate objects and the corresponding object attribute information, and the candidate object set includes the plurality of candidate objects involved in the retrieval library.

[0116] In a possible implementation, the apparatus 800 further includes: a construction unit;

[0117] The construction unit is used to construct a preset retrieval library.

[0118] As an example, the construction unit includes:

[0119] A second obtaining subunit is used to obtain a target image of each candidate object, wherein the target image includes only one candidate object;

[0120] A third obtaining subunit, configured to obtain object attribute information corresponding to each of the target images based on the feature detection model;

[0121] The saving subunit is used to save the mapping relationship between each candidate object and the corresponding object attribute information to obtain the search library.

[0122] In a possible implementation, the apparatus 800 further includes: a training unit;

[0123] The training unit is used to train the feature detection model.

[0124] As an example, the training unit includes:

[0125] A first training subunit is used to pre-train the initial model in a self-supervised manner based on the pictures in the training sample to obtain a first model;

[0126] A second training subunit is used to perform a one-stage training on the first model in a clustering manner based on the pictures in the training samples and the classification labels corresponding to the pictures to obtain a second model;

[0127] The third training subunit is used to perform at least one two-stage training on the second model using a binary classification method based on the two pictures in the training samples and the sample attributes of the two pictures, and record the second model obtained from the last two-stage training as the feature detection model.

[0128] In a possible implementation, the object combination includes a first combination of reference objects included in the collocation main image, and the device 800 further includes: an anchor unit;

[0129] The anchor point unit is used to add corresponding anchor points at preset positions of the areas where the reference objects in the matching main image are located, so as to obtain the combined image of the first combination, and the anchor points are used to associate with preset attribute information of the corresponding reference objects.

[0130] It should be noted that the specific implementation method of the device 800 and the technical effects achieved can be found in Figure 1 Description of the method shown.

[0131] In addition, an embodiment of the present application also provides an electronic device, which includes a processor and a memory: the memory is used to store instructions or computer programs; the processor is used to execute the instructions or computer programs in the memory, so that the electronic device executes any implementation of the method provided in the embodiment of the present application.

[0132] See also Fig. 9 , which shows a schematic diagram of the structure of an electronic device 900 suitable for implementing the embodiment of the present disclosure. The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig. 9 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0133] like Fig. 9 As shown, the electronic device 900 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage device 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 are also stored. The processing device 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0134] Typically, the following devices may be connected to the I / O interface 905: an input device 906 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 908 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 909. The communication device 909 may allow the electronic device 900 to communicate with other devices wirelessly or by wire to exchange data. Although Fig. 9 The electronic device 900 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0135] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 909, or installed from a storage device 908, or installed from a ROM 902. When the computer program is executed by the processing device 901, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0136] The electronic device provided by the embodiment of the present disclosure and the method provided by the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0137] The embodiment of the present application also provides a computer-readable medium, in which instructions or computer programs are stored. When the instructions or computer programs are executed on a device, the device executes any implementation of the method provided in the embodiment of the present application.

[0138] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0139] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0140] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0141] The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device can execute the method.

[0142] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0143] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0144] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit / module does not, in some cases, constitute a limitation on the unit itself.

[0145] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0146] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0147] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the system or device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0148] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0149] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0150] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0151] It should be noted that in the embodiments of the present application, user sensitive information is not involved, and user-related information is obtained, used and determined after user authorization. In one example, before obtaining user-related information, the corresponding interface displays prompt information related to obtaining data use authorization. The prompt information is based on relevant laws and regulations in an appropriate manner to inform the user of the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure, so that the user can determine whether to agree to the authorization based on the prompt information. It can be understood that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation method of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation method of the present disclosure.

[0152] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating an object combination, characterized in that: include: Acquire reference combination object information, wherein the reference combination object information includes object attribute information corresponding to each of a plurality of reference objects; For each reference object, determining a target candidate object corresponding to the reference object from a candidate object set according to the object attribute information corresponding to the reference object; wherein the degree of matching between the object attribute information of the target candidate object and the object attribute information corresponding to the reference object satisfies a preset condition; The target candidate objects respectively corresponding to the multiple reference objects are taken as an object combination.

2. The method according to claim 1, characterized in that The obtaining of reference combination object information includes: From the collocation main image, identifying a plurality of reference objects included in the collocation main image; According to the sub-picture corresponding to each reference object among the multiple reference objects in the matching main picture, object attribute information of the corresponding reference object is obtained.

3. The method according to claim 2, characterized in that The obtaining object attribute information of each reference object among the multiple reference objects according to the sub-picture corresponding to the reference object in the matching main picture includes: The sub-image corresponding to each reference object among the multiple reference objects in the matching main image is input into the feature detection model to obtain object attribute information of the corresponding reference object.

4. The method according to claim 3, characterized in that The step of determining a target candidate object corresponding to the reference object from a candidate object set according to the object attribute information corresponding to the reference object includes: From a preset retrieval library, object attribute information whose matching degree with the object attribute information corresponding to the reference object satisfies the preset conditions is determined, and the candidate object corresponding to the determined object attribute information is recorded as the target candidate object, the retrieval library includes a mapping relationship between each candidate object in a plurality of candidate objects and the corresponding object attribute information, and the candidate object set includes the plurality of candidate objects involved in the retrieval library.

5. The method according to claim 4, characterized in that The construction process of the preset search library includes: Obtaining a target image for each candidate object, wherein the target image includes only one candidate object; Based on the feature detection model, obtaining object attribute information corresponding to each of the target images; The mapping relationship between each candidate object and the corresponding object attribute information is saved accordingly to obtain the search library.

6. The method according to any one of claims 3 to 5, characterized in that: The training process of the feature detection model includes: Based on the pictures in the training samples, the initial model is pre-trained in a self-supervised manner to obtain a first model; Based on the pictures in the training samples and the classification labels corresponding to the pictures, the first model is trained in one stage by clustering to obtain a second model; Based on the two images in the training sample and the sample attributes of the two images, the second model is trained in two stages at least once using a binary classification method, and the second model obtained from the last two-stage training is recorded as the feature detection model.

7. The method according to any one of claims 2 to 6, characterized in that: The object combination includes a first combination of reference objects included in the collocation main image, and the method further includes: Corresponding anchor points are added at preset positions of the regions where the reference objects in the matching main image are located, respectively, to obtain the combined image of the first combination, wherein the anchor points are used to associate with preset attribute information of the corresponding reference objects.

8. A device for generating a combination of objects, characterized in that: include: An acquisition unit, configured to acquire reference combination object information; wherein the reference combination object information includes object attribute information corresponding to each of a plurality of reference objects; A matching unit, configured to determine, for each reference object, a target candidate object corresponding to the reference object from a candidate object set according to the object attribute information corresponding to the reference object; wherein the degree of matching between the object attribute information of the target candidate object and the object attribute information corresponding to the reference object satisfies a preset condition; The generating unit is used to combine the target candidate objects corresponding to each of the plurality of reference objects into an object combination.

9. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; The memory is used to store instructions or programs; The processor is used to execute the instructions or programs in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A readable medium, characterized in that: The readable medium stores instructions or programs, and when the instructions or programs are executed on a processor, the processor is enabled to execute the method according to any one of claims 1 to 7.

Images