Combined commodity retrieval method and system based on multi-modal pre-training model
A pre-training, multi-modal technology, applied in biological neural network models, business, character and pattern recognition, etc., can solve the problems of low accuracy and achieve the goal of improving accuracy, improving feature representation effect, and strong generalization Effect
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[0046] Example 1
[0047] as Figure 1 As shown in, a combined commodity retrieval method based on multimodal pre training model, the method comprises the following steps:
[0048] S1: divide the commodity image into single product image and combined product image, wherein the single product image represents only one commodity, and the combined product image representation includes a plurality of independent commodities;
[0049] S2: train a combined commodity image detector to detect each independent commodity in the combined commodity image;
[0050] S3: acquire and combine the feature code, position code and segment code of the text mode and picture module in the combined commodity image, so as to learn the embedded representation;
[0051] S4: build a multimodal pre training model and take the learned embedded representation as the input of the multimodal pre training model;
[0052] S5: the bounding box and bounding box features extracted by the commodity detector are used as ...
Example Embodiment
[0112] Example 2
[0113] Based on the combined commodity retrieval method of the multimodal pre training model described in embodiment 1, this embodiment also provides a combined commodity retrieval system of the multimodal pre training model. The system includes a sample construction module, an image detector training module, a learning embedded representation module, a multimodal pre training model module, a single product feature extraction module and a combined product feature extraction module; Among them,
[0114] The sample construction module is used to divide the commodity image into single product image and combined product image;
[0115] The image detector training module is used for training an image detector for detecting each independent commodity in the combined commodity image;
[0116] The learning embedded representation module is used to acquire and combine the feature coding, position coding and segment coding of the text mode and picture module in the combin...
Example Embodiment
[0124] Example 3
[0125] A computer system includes a memory, a processor and a computer program stored on the memory and running on the processor. When the processor executes the computer program, the method steps are as follows:
[0126] S1: divide the commodity image into single product image and combined product image, wherein the single product image represents only one commodity, and the combined product image representation includes a plurality of independent commodities;
[0127] S2: train a combined commodity image detector to detect each independent commodity in the combined commodity image;
[0128] S3: acquire and combine the feature code, position code and segment code of the text mode and picture module in the combined commodity image, so as to learn the embedded representation;
[0129] S4: build a multimodal pre training model and take the learned embedded representation as the input of the multimodal pre training model;
[0130] S5: the bounding box and bounding ...
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