解决多标签跨模态检索中类内聚集与类间结构的矛盾问题。
Deep Class-guided Hashing for Multi-label Cross-modal Retrieval
- 用代理损失保持类内紧凑性，配对损失维持类间结构。
- 引入方差约束缓解组合带来的语义偏差问题。
- 在三个基准数据集上性能优于或媲美现有方法。
深度哈希因其低成本和高效检索优势，在跨模态检索中备受关注。然而，现有跨模态哈希方法要么探索数据点间关系，导致类内分散；要么关注数据点与类别间关系，忽略类间结构保持，生成次优哈希码。针对此问题，本文提出一种深度类引导哈希（DCGH）方法：以代理损失为主保持类内聚集，结合配对损失维持类间结构关系，并进一步提出方差约束以缓解组合带来的语义偏差。在三个基准数据集上的大量对比实验表明，DCGH 方法性能可比甚至优于现有跨模态检索方法。代码已开源：https://github.com/donnotnormal/DCGH。
原文摘要 · Abstract (English)
Deep hashing, due to its low cost and efficient retrieval advantages, is widely valued in cross-modal retrieval. However, existing cross-modal hashing methods either explore the relationships between data points, which inevitably leads to intra-class dispersion, or explore the relationships between data points and categories while ignoring the preservation of inter-class structural relationships, resulting in the generation of suboptimal hash codes. How to maintain both intra-class aggregation and inter-class structural relationships, In response to this issue, this paper proposes a DCGH method. Specifically, we use proxy loss as the mainstay to maintain intra-class aggregation of data, combined with pairwise loss to maintain inter-class structural relationships, and on this basis, further propose a variance constraint to address the semantic bias issue caused by the combination. A large number of comparative experiments on three benchmark datasets show that the DCGH method has comparable or even better performance compared to existing cross-modal retrieval methods. The code for the implementation of our DCGH framework is available at https://github.com/donnotnormal/DCGH.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。