162 results about "Object Class" patented technology

The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing class-specific cascaded modulation inpainting neural network. For example, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network that includes cascaded modulation decoder layers to generate replacement pixels portraying a particular target object class. To illustrate, in response to user selection of a replacement region and target object class, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network corresponding to the target object class to generate an inpainted digital image that portrays an instance of the target object class within the replacement region. Moreover, in one or more embodiments the disclosed systems train class-specific cascaded modulation inpainting neural networks corresponding to a variety of target object classes, such as a sky object class, a water object class, a ground object class, or a human object class.

A text-to-image model training method, apparatus, and computer-readable storage medium for enhancing text-to-image generation through object-aware training. The method trains a text-to-image model using cyclic iterative training with sample image and text pairs. Training involves selecting image-text sample pairs containing multiple objects, obtaining corresponding mask images and object class names that distinguish location regions of the objects, and inputting both the sample image with description text and the mask images with object class names into the model. The method obtains image predicted noise and object predicted noises, constructs a loss function based on these predictions, and performs parameter adjustment accordingly. This approach enables improved object-level understanding in text-to-image generation models.

Systems and methods are provided for implementing training of a pre-trained object detection model for detecting new object classes. In examples, to train an object detection model, which has been pre-trained with a first set of object classes, with a new object class, a computing system applies to each of a plurality of first images that each depicts an object corresponding to an object class among the first set of object classes, a set of data augmentations combining each first image with at least one second image among a plurality of second images that each depicts a second object corresponding to the new object class, to generate a plurality of augmented images. The computing system trains the object detection model using the plurality of augmented images. In examples, original weights corresponding to the first set of object classes are retained, while random weights are used for the new object class.

A computer-implemented method for classifying points in a point cloud obtained by an active detection and ranging system is provided. The method comprises: computing a latent representation of the point cloud, determining from the latent representation, for each point in the point cloud, a probability that the point represents an object belonging to one or more object classes, and determining from the latent representation, for each point in the point cloud, a probability the point represents a ghost object. An object classification network is used for determining the probability that the point represents an object, and a ghost classification network is used for determining the probability the point represents a ghost object.

Approaches presented herein provide for the unconditional generation of novel three dimensional (3D) object shape representations, such as point clouds or meshes. In at least one embodiment, a first denoising diffusion model (DDM) can be trained to synthesize a 1D shape latent from Gaussian noise, and a second DDM can be trained to generate a set of latent points conditioned on this 1D shape latent. The shape latent and set of latent points can be provided to a decoder to generate a 3D point cloud representative of a random object from among the object classes on which the models were trained. A surface reconstruction process may be used to generate a surface mesh from this generated point cloud. Such an approach can scale to complex and / or multimodal distributions, and can be highly flexible as it can be adapted to various tasks such as multimodal voxel- or text-guided synthesis.

A computer-implemented method for building an object detection module uses mesh representations of objects belonging to specified object classes of interest to render images by a physics-based simulator. Each rendered image captures a simulated environment containing objects belonging to multiple object classes of interest placed in a bin or on a table. The rendered images are generated by randomizing a set of parameters by the simulator to render a range of simulated environments. The randomized parameters include environmental and sensor-based parameters. A label is generated for each rendered image, which includes a two-dimensional representation indicative of location and object classes of objects in that rendered image frame. Each rendered image and the respective label constitute a data sample of a synthetic training dataset. A deep learning model is trained using the synthetic training dataset to output object classes from an input image of a real-world physical environment.

The invention discloses an object recognition method and device, equipment and a storage medium, and relates to the technical field of machine learning, and the method comprises the steps: carrying out the feature similarity analysis of a target object feature of a to-be-recognized object and a class index feature group corresponding to a target object class, and obtaining target feature similarity data; the category index feature group is a clustering center feature corresponding to the positive sample object feature; the positive sample object is a sample object which is screened based on a feature density clustering result of a plurality of original sample objects and belongs to a target object category; based on the target feature similarity data and a preset feature similarity threshold, determining object category indication information of the to-be-recognized object; the preset feature similarity threshold value is obtained by performing threshold value adaptive adjustment based on a preset identification index threshold value and the sample feature similarity data; the sample feature similar data represents the feature similarity condition between the positive sample object feature of the positive sample object and the category index feature group. By using the scheme of the invention, accurate identification of a new category can be rapidly realized.

In order to at least partially automatically control the braking of a vehicle (1), object data containing an object class (9) of an object (6) in an environment of the vehicle (1) is generated or received by means of a data processing system (3) of the vehicle (1). By means of the data processing system (3), environment data (10) which contains statistical traffic accident data relating to the environment and / or relating to environment conditions of the environment is generated or received. By means of the data processing system (3), control data relating to a braking intensity for a desired braking maneuver of the vehicle (1) is determined according to the object class (9) and the environment data (10), and at least one control signal for at least one brake actuator of the vehicle (1) is generated according to the control data.

In a method for training a neural network for detecting an object, geometric dimensions of a test object from an object class are captured, and during a time period, recordings of the test object are generated by a plurality of cameras. From the captured geometric dimensions and the generated recordings, occupancy maps are generated. By a radar device, a radar signal is transmitted, and a radar signal reflected by the test object is received. The transmitted radar signal and the received radar signal are mixed into a complex baseband to form a mixed signal. A complex four-dimensional mixed spectrum of the mixed signal is calculated. From the complex four-dimensional mixed spectrum, a first complex two-dimensional partial spectrum and a second complex two-dimensional partial spectrum are calculated. The occupancy maps and the partial spectra are fusioned to form training data. The training data are fed to the neural network.

An autonomous driving system includes an object detection system. A neural network image encoder generates an image embedding associated with an image including an object. A neural network text encoder generates a concept embedding associated with each of a plurality of concepts. Each of the plurality of concepts is associated with one of at least two object classes. A confidence score module generates a confidence score for each of the plurality of concepts based on the image embedding and the concept embedding associated with the concept. An object class prediction module generates a predicted object class for the object based on an association between a concept set of the plurality of concepts having at least two of the highest values of the generated confidence scores and one of the at least two object classes associated with a majority of the concept set.

In a method for determining and classifying at least one object in a sensing area of a sensor, a two- or three-dimensional image of the sensing area is captured by means of the sensor. Sensor data corresponding to the image is generated. The object in the image is determined by means of the sensor data and by means of first template data of at least one first template object using a neural network. The neural network has been trained using the first template data. The first template data corresponds to an image of the first template object. At least one first object class is associated with the first template object. The object is classified by the neural network determining whether the object belongs to the first object class or not.

A method and system is disclosed for tuning a machine learning classifier. An object class requirement may be provided and include rank thresholds. The object class requirements may also include a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results. A base classifier may be trained using a weighted loss function that includes one or more weight values that are computed using the one or more object class requirements. An output of the weighted loss function may be evaluated using an objective function which may be established using the one or more object class requirements. The one or more weights may also be re-tuned using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for object detection using neural networks. In one aspect, one of the methods includes obtaining an input image; processing the input image using an sequence transduction neural network to generate an output sequence that comprises respective token at each of a plurality of time steps, wherein each token is selected from a vocabulary of tokens that comprises (i) a first set of tokens that each represent a respective discrete number from a set of discretized numbers and (ii) a second set of tokens that each represent a respective object category from a set of object categories; and generating, from the tokens in the output sequence, an object detection output for the input image.

The embodiment of the invention discloses a data processing method and device, electronic equipment and a computer readable storage medium. According to the embodiment of the invention, at least one training data pair of a target detection model is obtained, the training data pair comprises a text sample and an image sample, then a text object is extracted from the text sample, at least one object anchoring area where the text object is located is extracted from the image sample, and then the target detection model is obtained based on the object anchoring area. Clustering the text objects to obtain at least one object category, and calculating category bias information of each object category; when a to-be-detected image is obtained, performing target detection on the to-be-detected image by adopting the target detection model to obtain an initial detection result, and then adjusting the initial detection result by adopting the category bias information; according to the scheme, the target detection accuracy can be improved; the embodiment of the invention can be applied to various scenes such as artificial intelligence, computer vision, natural language processing and the like.

Included is a method for estimating distance, including: emitting, with at least one light emitter disposed on a robot, a light structure onto objects; capturing, with at least one image sensor disposed on the robot, images of the light structure emitted onto the objects; identifying, with a processor, the light structure within the images; determining, with the processor, positions of elements of the light structure within the images; determining, with the processor, a characteristic relating to the objects based on positions of elements of the light structure within the images; determining, with the processor, an object class of at least one object within the images based on a comparison between features of the object extracted from the images and an object dictionary comprising various object classes and their associated features; and instructing, with the processor, the robot to execute at least one action based on the object class identified.

Examples of the present disclosure describe systems and methods for sensory and response modeling in OWT systems. In examples, a payload is received by a sensory machine learning (ML) model implemented within an OWT system. The sensory ML model outputs an indication associated with data within the payload, such as whether the data belongs to one or more object classes or is indicative of anomalous activity. The output of the sensory ML model is provided to a response ML model implemented within the OWT system. The response ML model outputs a determination associated with the payload, such as whether the payload is permitted to egress across a data boundary of the OWT system or the manner in which data in the payload can be used in the one or more computing environments. The payload is then processed in accordance with the determination.

A computer system and method for training a machine learning system to perform a classification task by dividing input data into one of a plurality of classes. The system is configured to receive training data for each class from which a representation of each class can be derived, wherein each class is described by a plurality of representations; process the training data to form, for at least one class, a first proxy for a relatively global portion of training data items and a plurality of proxies for different relatively local portions of training data items, each proxy corresponding to a representation of data belonging to the class. For each training data item, the system is configured to: evaluate a match between the training data item and the proxies; estimate the class of the training data item from the match rating; adjust the proxies by updating a weighting matrix to reduce the distance between the training data item and the proxies of the estimated class. Defining a plurality of proxies in this way can result in richer and more stable representations of object classes.

Methods for automated process monitoring, wherein - image data (4) depicting a scene are generated by means of a camera system (2); - by means of a computing unit (3) based on the image data (4) it is determined that objects (6a, 6b, 7a, 7b, 7c, 8, 9, 10, 11, 14) are located within a specified area (B1, B2); - by means of the computing unit (3) for each of the objects (6a, 6b, 7a, 7b, 7c, 8, 9, 10, 11, 14) based on the image data (4) one of at least two predefined object classes is determined; - using the computing unit (3) depending on the specific object classes, it is checked whether a predefined rule assigned to the area (B1, B2) is fulfilled; - by means of the computing unit (3) an output signal is generated depending on a result of the check; - using the computing unit (3) based on the image data (4) for each of the objects (6a, 6b, 7a, 7b, 7c, 8, 9, 10, 11, 14) one of at least two predefined sub-areas (B1.1, B1.1.1, B1.2, B2.1, B2.1.1, B2.2, B2.2.1) of the area (B1, B2) is determined within which the respective object (6a, 6b, 7a, 7b, 7c, 8, 9, 10, 11, 14) is located; and - the verification of whether the rule is fulfilled is carried out depending on the specific sub-areas (B1.1, B1.1.1, B1.2, B2.1, B2.1.1, B2.2, B2.2.1).

The present invention relates to a computer-implemented method of training an object detector (OD), an object detector (OD), a computer program and a computer-readable (storage) medium. In order to carry out the method, a set of object classes (C) obfuscated by a neural network (NN), an object feature map (FM) originating from the neural network (NN), and object class tags (LO) comprising object classes (C) assigned to each object (O) in the object feature map (FM) need to be acquired. Subsequently, a training head (TH) configured to determine the authenticity of an object class (C) assigned to the object (O) in the object feature map (FM) from the object feature map (FM) and the object class tag (LO) is added to the neural network (NN). A modified object category tag (LO ') is determined from the set of object categories (C) and the object category tag (LO), wherein at least one object category assignment (C) is modified. The modified object category tags (LO ') are processed together with the object feature map (FM) by a neural network, generating an output, which is compared with an authenticity tag (LT) using a first objective function (OF1), the authenticity tag comprising an indication of the authenticity of each object category (C) in the modified object category tags (LO'). And updating the network parameters according to the comparison result.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text-to-image model so that the text-to-image model generates images that each depict a variable instance of an object class when the object class without the unique identifier is provided as a text input, and that generates images that each depict a same subject instance of the object class when the unique identifier is provided as the text input.

The present disclosure relates to enabling active learning for object classification by an automotive vision system configured to perform visual perception tasks, based on which a vehicle is configured to perform at least one driving automation system feature. The automotive vision system determines one or more 3D bounding boxes and a corresponding object class within three-dimensional sets of automotive sensor data, which including at least one automotive camera frame, i.e. two-dimensional data, while a secondary vision system determines one or more 2D bounding box vectors for each automotive camera frame. Based at least on the one or more 2D bounding box vectors, a frame score is calculated for each automotive camera frame. Then, one or more automotive camera frames are provided to an oracle based on the corresponding frame scores. The oracle returns annotated camera frames, which may then be used to retrain the automotive vision system.

A method and related system may analyze metadata associated with a first set of transactions to determine whether to perform a second set of transactions. The method and related system may include determining a data category based on first device-provided data of a first database transaction indicating a first record associated with a second record, and may further include obtaining, based on whether the data category satisfies a first set of criteria, an identifier and an amount based on image data using a prediction model. The method may further include querying a database based on the identifier to obtain an indication that the identifier is mapped to an object category, validating the amount based on a result indicating whether the object category satisfies a second set of criteria, and causing a second transaction that changes fields of the first and second records.

The application discloses an object recognition method and device, a storage medium and an electronic device. The method comprises the following steps: using a trained object recognition network to perform multiple times of recognition processing on a road image containing an object to be processed, and obtaining multiple pieces of object recognition information; wherein the object recognition information at least contains predicted key point information and category information obtained after the object to be processed is predicted; the object recognition network is obtained based on the difference information between the predicted key point information of a historical object in a sample road image and corresponding labeled key point information; and based on the multiple pieces of object recognition information, the object category, position information and key point obtained after the object to be processed is recognized are determined. The application solves the technical problem of low vehicle recognition efficiency caused by the multi-stage recognition technology in the related art.

A semantic image segmentation (SIS) system includes: a semantic segmentation module trained to segment objects belonging to predetermined classes in input images using training images; and a learning module configured to selectively update at least one parameter of each of a localizer module, an encoder module, and a decoder module of the semantic segmentation module to identify objects having a new class that is not one of the predetermined classes: based on an image level class for a learning image including an object having the new class that is not one of the predetermined classes; and without a pixel-level annotation for the learning image.

Environment sensing system, comprising at least one sensor (10) for providing location data (12) about objects (18), wherein the location data (12) includes velocity data, and comprising a computational means (14) for converting the location data (12) into an environment model (16) that represents kinematic object data of the objects (18) and assigns an object (18) to a respective object class (K), wherein the computational means (14) is conditioned to preferentially output environment models (16) in which, for an object (18) corresponding to at least one predefined object class (K), at least one predefined physical relationship of the object data to location data (12) assigned to the object (18) is better satisfied for those location data (12) assigned to the object (18) that correspond to a predefined association criterion than for the totality of the location data (12) assigned to the object (18).

The application relates to the technical field of artificial intelligence, and provides an object recognition method, device and equipment and a storage medium. Related embodiments can be applied to cloud technology, cloud security, artificial intelligence, intelligent transportation and the like, more model training samples are mined from massive data, and the prediction accuracy of a model for a user category is improved. The method comprises the following steps: extracting candidate object features from multiple candidate object information of a candidate object; performing object category possibility degree recognition on object extraction features obtained by fusing the candidate object features, to obtain a recognition possibility degree of the candidate object belonging to a target object category; clustering the object extraction features corresponding to the candidate object, to obtain a sub-extraction feature set corresponding to each clustering category; grouping candidate objects corresponding to the object extraction features in the sub-extraction feature set into a sub-object set; and selecting a representative object from the sub-object set based on the recognition possibility degrees of the candidate objects corresponding to the sub-object set.

A few-shot, unsupervised image-to-image translation (“FUNIT”) algorithm is disclosed that accepts as input images of previously-unseen target classes. These target classes are specified at inference time by only a few images, such as a single image or a pair of images, of an object of the target type. A FUNIT network can be trained using a data set containing images of many different object classes, in order to translate images from one class to another class by leveraging few input images of the target class. By learning to extract appearance patterns from the few input images for the translation task, the network learns a generalizable appearance pattern extractor that can be applied to images of unseen classes at translation time for a few-shot image-to-image translation task.

A media curation system configured to perform operations that include, capturing an image at a client device, wherein the image includes a depiction of an object, identifying an object category of the object based on the depiction of the object within the image, accessing media content associated with the object category within a media repository, generating a presentation of the media content, and causing display of the presentation of the media content within the image at the client device.

Method for creating an environment model of a vehicle, characterized in that sensor data from a sensor system are received (101), at least one object is determined (102) based on the sensor data, the object is displayed to an observer, in particular the driver, after the vehicle has been driven (103) and the object is assigned an object class from a predefined list of object classes by the observer (104).

The invention relates to a space target attitude key point data set construction method and system based on an unreal engine. The method comprises the following steps: constructing a three-dimensional model of a space target, preprocessing the three-dimensional model, and importing the preprocessed three-dimensional model into the unreal engine; creating a user-defined marking component in the unreal engine for defining key points of the space target in the preprocessed three-dimensional model, and constructing a user-defined recognition object class for managing the key points of the space target; wherein the management comprises automatic detection, classified storage and sorting of key points of space targets; creating a space scene in the unreal engine, constructing an animation sequence used for simulating the motion trail and posture change of the space target in the space scene, and obtaining a plurality of space target images based on the animation sequence; and generating structured space target attitude key point data based on the space target image, and forming a space target attitude key point data set by taking the corresponding space target image as sample data and taking the extended marking attribute of the custom marking component as a sample label.