Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous modality data into the binary hash codes with discriminative semantic labels. It can support efficient large-scale cross-modal retrieval due to the fast retrieval speed and low storage cost. However, existing methods equally handle the cross-modal retrieval tasks, and simply learn the same couple of hash functions in a symmetric way. Under such circumstances, the characteristics of different cross-modal retrieval tasks are ignored and sub-optimal performance may be brought. Motivated by this, we present a Task-adaptive Asymmetric Deep Cross-modal Hashing (TA-ADCMH) method in this paper. It can learn task-adaptive hash functions for two sub-retrieval tasks via simultaneous modality representation and asymmetric hash learning. Different from previous cross-modal hashing methods, our learning framework jointly optimizes the semantic preserving from multi-modal features to the hash codes, and the semantic regression from query modality representation to the explicit labels. With our model, the learned hash codes can effectively preserve the multi-modal semantic correlations, and meanwhile, adaptively capture the query semantics. Besides, we design an efficient discrete optimization strategy to directly learn the binary hash codes, which alleviates the relaxing quantization errors. Extensive experiments demonstrate the state-of-the-art performance of the proposed TA-ADCMH from various aspects.

有监督的跨模态散列旨在将具有异构语义标签的异构模态数据的语义相关性嵌入到二进制散列码中。它具有快速的检索速度和较低的存储成本，因此可以支持有效的大规模跨模式检索。但是，现有方法同样可以处理交叉模式检索任务，并且可以对称方式简单地学习相同的哈希函数对。在这种情况下，将忽略不同的跨模式检索任务的特征，并且可能会带来次优的性能。为此，本文提出了一种任务自适应的不对称深交叉模式散列（TA-ADCMH）方法。它可以通过同时模态表示和非对称哈希学习为两个子检索任务学习自适应任务的哈希函数。与以前的跨模式散列方法不同，我们的学习框架共同优化了从多模式特征到哈希码的语义保留，以及从查询模态表示到显式标签的语义回归。利用我们的模型，学习到的哈希码可以有效地保留多模式语义相关性，同时自适应地捕获查询语义。此外，我们设计了一种有效的离散优化策略来直接学习二进制哈希码，从而减轻了松弛的量化误差。广泛的实验从各个方面证明了所提出的TA-ADCMH的最新性能。以及从查询模态表示到显式标签的语义回归。利用我们的模型，学习到的哈希码可以有效地保留多模式语义相关性，同时自适应地捕获查询语义。此外，我们设计了一种有效的离散优化策略来直接学习二进制哈希码，从而减轻了松弛的量化误差。广泛的实验从各个方面证明了所提出的TA-ADCMH的最新性能。以及从查询模态表示到显式标签的语义回归。利用我们的模型，学习到的哈希码可以有效地保留多模式语义相关性，同时自适应地捕获查询语义。此外，我们设计了一种有效的离散优化策略来直接学习二进制哈希码，从而减轻了松弛的量化误差。广泛的实验从各个方面证明了所提出的TA-ADCMH的最新性能。