Text-to-Image Person Reidentification Based on Attribute Dependency Augmentation
Text-to-Image Person Reidentification(TIPR)aims to retrieve a target person from a pedestrian gal-lery with a given text,and its main challenge is to learn the robust features of free-view(posture,lighting and cam-era viewpoint)images and free-form texts.However,due to the lack of pedestrian attribute mining in text descrip-tions and pedestrian images,the retrieval performance from text descriptions to pedestrian images is affected by dif-ferences in details in fine granularity.Therefore,this study proposes TIPR based on Attribute Dependency Aug-mentation(ADA).Firstly,it analyzes dependencies from text descriptions and transforms them into dependency ma-trixes.Then,it designs an attribute intervention module based on self-attention to fuse text features and depen-dency matrixes and obtains attribute-augmented text features which are more concerned about attribute informa-tion after intervention.Finally,it allows text features and image features participate in training,making the whole network more sensitive to attribute mining.Experiments on two datasets CUHK-PEDES and ICFG-PEDES dem-onstrate the effectiveness of the proposed model.
Text-to-Image Person ReidentificationSelf-attention mechanismSyntactic dependencyFree view
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NSTL
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