Due to the high efficiency of hashing technology and the high abstraction of deep networks, deep hashing has achieved appealing effectiveness and efficiency for large-scale cross-modal retrieval. However, how to efficiently measure the similarity of fine-grained multi-labels for multi-modal data and thoroughly explore the intermediate layers specific information of networks are still two challenges for high-performance cross-modal hashing retrieval. Thus, in this paper, we propose a novel Hierarchical Semantic Interaction-based Deep Hashing Network (HSIDHN) for large-scale cross-modal retrieval. In the proposed HSIDHN, the multi-scale and fusion operations are first applied to each layer of the network. A Bidirectional Bi-linear Interaction (BBI) policy is then designed to achieve the hierarchical semantic interaction among different layers, such that the capability of hash representations can be enhanced. Moreover, a dual-similarity measurement (“hard” similarity and “soft” similarity) is designed to calculate the semantic similarity of different modality data, aiming to better preserve the semantic correlation of multi-labels. Extensive experiment results on two large-scale public datasets have shown that the performance of our HSIDHN is competitive to state-of-the-art deep cross-modal hashing methods.

中文翻译：

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基于层次语义交互的深度哈希网络用于跨模式检索

由于哈希技术的高效性和深度网络的高度抽象性，深度哈希对于大规模的跨模式检索已实现了引人注目的有效性和效率。然而，如何有效地测量多模式数据的细粒度多标签的相似性并彻底探索中间层的网络特定信息仍然是高性能跨模式哈希检索的两个挑战。因此，在本文中，我们提出了一种新颖的基于层次语义交互的深度哈希网络（HSIDHN），用于大规模的交叉模式检索。在提出的HSIDHN中，首先将多尺度和融合操作应用于网络的每一层。然后设计了双向双向线性交互（BBI）策略，以实现不同层之间的分层语义交互，这样就可以增强哈希表示的功能。此外，设计了双相似度度量（“硬”相似度和“软”相似度）以计算不同模态数据的语义相似度，旨在更好地保留多标签的语义相关性。在两个大型公共数据集上的大量实验结果表明，我们的HSIDHN的性能与最新的深层交叉模式哈希方法相比具有竞争力。