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Causal Inference Hashing for Long-Tailed Image Retrieval.

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Title: Causal Inference Hashing for Long-Tailed Image Retrieval.
Authors: Jin, Lu1 lu.jin@njust.edu.cn, Lu, Zhengyun1 zhengyunlu@njust.edu.cn, Li, Zechao1 zechao.li@njust.edu.cn, Pan, Yonghua1 yonghuapan@njust.edu.cn, Dai, Longquan1 dailongquan@njust.edu.cn, Tang, Jinhui2 tangjh@njfu.edu.cn, Jain, Ramesh3 jain@ics.uci.edu
Source: IEEE Transactions on Image Processing. 2025, Vol. 34, p5099-5114. 16p.
Subjects: Image retrieval, Hashing, Information retrieval, Electronic file management, Multimedia systems
Abstract: In hashing-based long-tailed image retrieval, the dominance of data-rich head classes often hinders the learning of effective hash codes for data-poor tail classes due to inherent long-tailed bias. Interestingly, this bias also contains valuable prior knowledge by revealing inter-class dependencies, which can be beneficial for hash learning. However, previous methods have not thoroughly analyzed this tangled negative and positive effects of long-tailed bias from a causal inference perspective. In this paper, we propose a novel hash framework that employs causal inference to disentangle detrimental bias effects from beneficial ones. To capture good bias in long-tailed datasets, we construct hash mediators that conserve valuable prior knowledge from class centers. Furthermore, we propose a de-biased hash loss To enhance the beneficial bias effects while mitigating adverse ones, leading to more discriminative hash codes. Specifically, this loss function leverages the beneficial bias captured by hash mediators to support accurate class label prediction, while mitigating harmful bias by blocking its causal path to the hash codes and refining predictions through backdoor adjustment. Extensive experimental results on four widely used datasets demonstrate that the proposed method improves retrieval performance against the state-of-the-art methods by large margins. The source code is available at https://github.com/IMAG-LuJin/CIH [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In hashing-based long-tailed image retrieval, the dominance of data-rich head classes often hinders the learning of effective hash codes for data-poor tail classes due to inherent long-tailed bias. Interestingly, this bias also contains valuable prior knowledge by revealing inter-class dependencies, which can be beneficial for hash learning. However, previous methods have not thoroughly analyzed this tangled negative and positive effects of long-tailed bias from a causal inference perspective. In this paper, we propose a novel hash framework that employs causal inference to disentangle detrimental bias effects from beneficial ones. To capture good bias in long-tailed datasets, we construct hash mediators that conserve valuable prior knowledge from class centers. Furthermore, we propose a de-biased hash loss To enhance the beneficial bias effects while mitigating adverse ones, leading to more discriminative hash codes. Specifically, this loss function leverages the beneficial bias captured by hash mediators to support accurate class label prediction, while mitigating harmful bias by blocking its causal path to the hash codes and refining predictions through backdoor adjustment. Extensive experimental results on four widely used datasets demonstrate that the proposed method improves retrieval performance against the state-of-the-art methods by large margins. The source code is available at https://github.com/IMAG-LuJin/CIH [ABSTRACT FROM AUTHOR]
ISSN:10577149
DOI:10.1109/TIP.2025.3588054