Robust Contrastive Cross-modal Hashing with Noisy Labels

Cross-modal hashing has emerged as a promising technique for retrieving relevant information across distinct media types thanks to its low storage cost and high retrieval efficiency. However, the success of most existing methods heavily relies on large-scale well-annotated datasets, which are costly and scarce in the real world due to ubiquitous labeling noise. To tackle this problem, in this paper, we propose a novel framework, termed Noise Resistance Cross-modal Hashing (NRCH), to learn hashing with noisy labels by overcoming two key challenges, i.e. noise overfitting and error accumulation. Specifically, i) to mitigate the overfitting issue caused by noisy labels, we present a novel Robust Contrastive Hashing loss (RCH) to target homologous pairs instead of noisy positive pairs, thus avoiding overemphasizing noise. In other words, RCH enforces the model focus on more reliable positives instead of unreliable ones constructed by noisy labels, thereby enhancing the robustness of the model against noise; ii) to circumvent error accumulation, a Dynamic Noise Separator (DNS) is proposed to dynamically and accurately separate the clean and noisy samples by adaptively fitting the loss distribution, thus alleviate the adverse influence of noise on iterative training. Finally, we conduct extensive experiments on four widely used benchmarks to demonstrate the robustness of our NRCH against noisy labels for cross-modal retrieval. The code is available at: https://github.com/LonganWANG-cs/NRCH.git.