Long-Tailed Approaching Cross-Modal Hashing With Multi-Expert Collaborative Learning

Cross-modal hashing enables efficient retrieval across different modalities by mapping heterogeneous data into compact binary codes within a shared Hamming space. However, most existing methods assume that data from each class are evenly distributed, which contradicts the long-tailed nature of real-world data. Consequently, these approaches often exhibit suboptimal performance when handling imbalanced datasets. The only existing cross-modal hashing method that considers long-tailed data attempts to mine both the individuality and commonality across modalities, yet it relies on a negative log-likelihood pairwise loss that tends to bias the model toward head categories. To address this issue, we propose a novel Long-tailed Approaching Cross-modal Hashing (LACH) framework based on multi-expert collaborative learning. Specifically, LACH constructs a multi-expert architecture with a Graph Convolutional Network (GCN) backbone to facilitate knowledge transfer. Unlike conventional multi-expert models that either share identical data copies or employ entirely distinct data subsets, we introduce a partial data replication strategy that ensures each expert receives a balanced yet overlapping training set. Furthermore, we design a proxy-based pointwise loss to treat head and tail categories equitably, along with an inter-modal approaching loss to enhance the alignment of hash codes across modalities within each class. Extensive experiments demonstrate that LACH achieves accuracy improvements of up to 4.2% and 6.3% over state-of-the-art baselines on balanced and long-tailed datasets, respectively. Our code is available at https://github.com/caoyuan618/LACH.