论文标题
自适应边缘化的语义散列，用于未配对的跨模式检索
Adaptive Marginalized Semantic Hashing for Unpaired Cross-Modal Retrieval
论文作者
论文摘要
近年来，跨模式哈希（CMH）由于其快速查询速度和有效的存储而引起了很多关注。以前的文献通过发现歧视性哈希码和模态特定的哈希功能，为跨模式检索（CMR）实现了有希望的结果。 Nonetheless, most existing CMR works are subjected to some restrictions: 1) It is assumed that data of different modalities are fully paired, which is impractical in real applications due to sample missing and false data alignment, and 2) binary regression targets including the label matrix and binary codes are too rigid to effectively learn semantic-preserving hash codes and hash functions.为了解决这些问题，本文提出了一种自适应的语义哈希（AMSH）方法，该方法不仅通过自适应边缘增强了潜在表示和哈希码的歧视，而且还可以用于配对和未配对的CMR。作为一种两步方法，第一步，AMSH生成具有适应性边缘化标签的语义感知模态特异性的潜在表示，从而扩大了不同类别之间的距离，并利用标签来保留模式间和模式内模式的语义相似性到潜在的代表和哈希代码。在第二步中，自适应边缘矩阵嵌入了哈希码中，并扩大正面和负位之间的差距，从而改善了哈希功能的歧视和鲁棒性。在此基础上，AMSH生成了相似性提供的哈希码和鲁棒的哈希功能，而无需严格的一对一数据对应关系。实验是在几个基准数据集上进行的，以证明AMSH比某些最新CMR方法的优越性和灵活性。
In recent years, Cross-Modal Hashing (CMH) has aroused much attention due to its fast query speed and efficient storage. Previous literatures have achieved promising results for Cross-Modal Retrieval (CMR) by discovering discriminative hash codes and modality-specific hash functions. Nonetheless, most existing CMR works are subjected to some restrictions: 1) It is assumed that data of different modalities are fully paired, which is impractical in real applications due to sample missing and false data alignment, and 2) binary regression targets including the label matrix and binary codes are too rigid to effectively learn semantic-preserving hash codes and hash functions. To address these problems, this paper proposes an Adaptive Marginalized Semantic Hashing (AMSH) method which not only enhances the discrimination of latent representations and hash codes by adaptive margins, but also can be used for both paired and unpaired CMR. As a two-step method, in the first step, AMSH generates semantic-aware modality-specific latent representations with adaptively marginalized labels, which enlarges the distances between different classes, and exploits the labels to preserve the inter-modal and intra-modal semantic similarities into latent representations and hash codes. In the second step, adaptive margin matrices are embedded into the hash codes, and enlarge the gaps between positive and negative bits, which improves the discrimination and robustness of hash functions. On this basis, AMSH generates similarity-preserving hash codes and robust hash functions without strict one-to-one data correspondence requirement. Experiments are conducted on several benchmark datasets to demonstrate the superiority and flexibility of AMSH over some state-of-the-art CMR methods.
