Weighted Contrastive Hashing
Jiaguo Yu, Huming Qiu, Dubing Chen, Haofeng Zhang

TL;DR
This paper introduces Weighted Contrastive Hashing (WCH), a novel unsupervised hashing method that improves image retrieval by leveraging fine-grained semantic relations and a mutual attention module to address limitations of previous contrastive learning approaches.
Contribution
The paper proposes a new WCH method with a mutual attention module and patch-based similarity aggregation to enhance unsupervised hashing performance.
Findings
WCH outperforms existing methods on benchmark datasets.
The mutual attention module improves feature information utilization.
Patch-based similarity enhances semantic relation modeling.
Abstract
The development of unsupervised hashing is advanced by the recent popular contrastive learning paradigm. However, previous contrastive learning-based works have been hampered by (1) insufficient data similarity mining based on global-only image representations, and (2) the hash code semantic loss caused by the data augmentation. In this paper, we propose a novel method, namely Weighted Contrative Hashing (WCH), to take a step towards solving these two problems. We introduce a novel mutual attention module to alleviate the problem of information asymmetry in network features caused by the missing image structure during contrative augmentation. Furthermore, we explore the fine-grained semantic relations between images, i.e., we divide the images into multiple patches and calculate similarities between patches. The aggregated weighted similarities, which reflect the deep image relations,…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods · Multimodal Machine Learning Applications
MethodsContrastive Learning
