Instance-Dependent Noise Robust Hashing for Multi-Label Image Retrieval

Supervised hashing has achieved remarkable progress in image retrieval, yet its performance heavily relies on high-quality labels and is therefore vulnerable to label noise. Existing robust hashing methods mainly address class-conditional noise (CCN), while instance-dependent noise (IDN), which better reflects real-world noise patterns, remains unexplored in multi-label hashing retrieval. We identify two fundamental corruptions of pairwise supervision caused by multi-label IDN: additive IDN introduces spurious semantic similarities, causing false attraction, whereas subtractive IDN destroys genuine semantic correlations, resulting in false repulsion. To address them, we propose Instance-Dependent Noise Robust Hashing (IDNRH). It consists of two complementary components: Sample-to-Label Truncation (SLT), which uses a GMM to partition potentially noisy samples, reconstructs label confidence through multi-view neighbor consensus, and truncates unreliable labels to mitigate false attraction; and Repulsion-Filtered Soft Contrastive Learning (RFSCL), which suppresses false repulsion using shared-neighbor similarity while adaptively modulating attraction with soft label similarity. Extensive experiments on widely used datasets and a real-world noisy dataset demonstrate that IDNRH consistently outperforms state-of-the-art hashing methods under multi-label IDN.