Two-Step Discrete Hashing for Cross-Modal Retrieval

Cross-modal hashing is an effective approach for information retrieval from large and heterogeneous cross-modal datasets, owing to its low storage cost and high computational speed. However, conventional cross-modal hashing techniques for generating hashing codes rely on cross-space dimensional compression, which results in two types of information loss: quantization information loss and dimension reduction loss. To address these limitations, we propose a novel method that decouples the one-step hashing (Fig.1a) strategy into two sub-steps (Fig.1b). Specifically, in the first step, we introduce a novel differentiable hash method, which utilizes a smooth hash module for binary quantization. This method allows our model to reduce the quantization information loss and make the model optimized by gradient descent. In the second step, we design a long-short Hamming space transformation approach to project the long code into a short one, which is effective in preserving the dimension information between long and short and mitigating the dimension reduction loss. We demonstrate the effectiveness of our approach through extensive experiments on several popular cross-modal datasets, achieving a significant improvement in cross-modal retrieval performance.