任务自适应非对称深度交叉模式散列(CS.IR)---蔡小雪7100294

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。由于其在检索和存储效率方面的优势，它被广泛用于解决有效的跨模式检索。但是，现有研究平等地处理了跨模式检索的不同任务，并为它们简单地以对称的方式学习了相同的哈希函数对。在这种情况下，不同的交叉模式检索任务的唯一性将被忽略，并可能带来次优的性能。因此，本文提出了一种任务自适应的不对称深交叉模式哈希（TA-ADCMH）方法。它可以通过同时模态表示和非对称哈希学习来学习两个子检索任务的任务自适应哈希函数。与以前的跨模式哈希方法不同，我们的学习框架共同优化了将多媒体数据的深层特征转换为二进制哈希码的语义保留，以及将查询模态表示直接回归到显式标签的语义回归。利用我们的模型，二进制代码可以有效地保留不同模式之间的语义相关性，同时自适应地捕获查询语义。 TA-ADCMH的优越性已从多个方面在两个标准数据集中得到证明。

原文标题：Task-adaptive Asymmetric Deep Cross-modal Hashing

原文：Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous modality data into the binary hash codes with discriminative semantic labels. Because of its advantages on retrieval and storage efficiency, it is widely used for solving efficient cross-modal retrieval. However, existing researches equally handle the different tasks of cross-modal retrieval, and simply learn the same couple of hash functions in a symmetric way for them. Under such circumstance, the uniqueness 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. Unlike previous cross-modal hashing approaches, our learning framework jointly optimizes semantic preserving that transforms deep features of multimedia data into binary hash codes, and the semantic regression which directly regresses query modality representation to explicit label. With our model, the binary codes can effectively preserve semantic correlations across different modalities, meanwhile, adaptively capture the query semantics. The superiority of TA-ADCMH is proved on two standard datasets from many aspects.

原文作者：Tong Wang, Lei Zhu, Zhiyong Cheng, Jingjing Li, Huaxiang Zhang

原文地址：https://arxiv.org/abs/2004.00197