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Due to their high retrieval efficiency and low storage cost for cross-modalsearch task, cross-modal hashing methods have attracted considerable attention.For the supervised cross-modal hashing methods, how to make the learned hashcodes preserve semantic information sufficiently contained in the label ofdatapoints is the key to further enhance the retrieval performance. Hence,almost all supervised cross-modal hashing methods usually depends on defining asimilarity between datapoints with the label information to guide the hashingmodel learning fully or partly. However, the defined similarity betweendatapoints can only capture the label information of datapoints partially andmisses abundant semantic information, then hinders the further improvement ofretrieval performance. Thus, in this paper, different from previous works, wepropose a novel cross-modal hashing method without defining the similaritybetween datapoints, called Deep Cross-modal Hashing via\textit{Margin-dynamic-softmax Loss} (DCHML). Specifically, DCHML first trainsa proxy hashing network to transform each category information of a datasetinto a semantic discriminative hash code, called proxy hash code. Each proxyhash code can preserve the semantic information of its corresponding categorywell. Next, without defining the similarity between datapoints to supervise thetraining process of the modality-specific hashing networks , we propose a novel\textit{margin-dynamic-softmax loss} to directly utilize the proxy hashingcodes as supervised information. Finally, by minimizing the novel\textit{margin-dynamic-softmax loss}, the modality-specific hashing networkscan be trained to generate hash codes which can simultaneously preserve thecross-modal similarity and abundant semantic information well.

Deep Cross-modal Hashing via Margin-dynamic-softmax Loss