Evaluation on Fine-grained Cross Modal Image and Text Retrieval Method Based on Deep Adversarial Hashing Technology

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Fine-grained cross modal image and text retrieval is one of the hot topics in recent years, with the goal of achieving efficient and accurate image and text retrieval in different modal datasets. In this paper, a new type of cross-modal graphic retrieval method based on deep adversarial hashing is used to learn the feature representation of images and text through deep network, and realize the feature hash representation through adversarial training. The adversary network is introduced into the network structure. By maximizing the mutual information of hash coding and minimizing the anti classification loss of hash coding, the feature representation of image and text is highly consistent and separable in the hash coding space. Experiments have proved that compared with the existing algorithm, this algorithm can be retrieved better.