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公开(公告)号：EP3923185A3
公开(公告)日：2022-04-27
申请号：EP21202754.4
申请日：2021-10-14
发明人： YU, Yuechen , ZHANG, Chengquan , LI, Yulin , ZHANG, Xiaoqiang , HUANG,, Ju , QIN, Xiameng , YAO, Kun , LIU, Jingtuo , HAN, Junyu , DING, Errui
摘要： Provided are an image classification method and apparatus, an electronic device and a storage medium, relating to the field of artificial intelligence and, in particular, to computer vision and deep learning. The method includes inputting (S101, S201, S301) a to-be-classified document image into a pretrained neural network and obtaining a feature submap of each text box of the to-be-classified document image by use of the neural network; inputting (S102, S202, S302) the feature submap of each text box, a semantic feature corresponding to preobtained text information of each text box and a position feature corresponding to preobtained position information of each text box into a pretrained multimodal feature fusion model and fusing, by use of the multimodal feature fusion model, the three into a multimodal feature corresponding to each text box; and classifying (S103) the to-be-classified document image based on the multimodal feature corresponding to each text box. The semantic feature and position feature in the document image are well used so that the object of improving the classification accuracy of the document image is achieved.
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公开(公告)号：EP3923185A2
公开(公告)日：2021-12-15
申请号：EP21202754.4
申请日：2021-10-14
发明人： YU, Yuechen , ZHANG, Chengquan , LI, Yulin , ZHANG, Xiaoqiang , HUANG,, Ju , QIN, Xiameng , YAO, Kun , LIU, Jingtuo , HAN, Junyu , DING, Errui
摘要： Provided are an image classification method and apparatus, an electronic device and a storage medium, relating to the field of artificial intelligence and, in particular, to computer vision and deep learning. The method includes inputting (S101, S201, S301) a to-be-classified document image into a pretrained neural network and obtaining a feature submap of each text box of the to-be-classified document image by use of the neural network; inputting (S102, S202, S302) the feature submap of each text box, a semantic feature corresponding to preobtained text information of each text box and a position feature corresponding to preobtained position information of each text box into a pretrained multimodal feature fusion model and fusing, by use of the multimodal feature fusion model, the three into a multimodal feature corresponding to each text box; and classifying (S103) the to-be-classified document image based on the multimodal feature corresponding to each text box. The semantic feature and position feature in the document image are well used so that the object of improving the classification accuracy of the document image is achieved.
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