Differentiable Cross-modal Hashing via Multimodal Transformers
Junfeng Tu, Xueliang Liu, Zongxiang Lin, Richang Hong, Meng Wang
Abstract
Cross-modal hashing aims at projecting the cross modal content into a common Hamming space for efficient search. Most existing work first encodes the samples with a deep network and then binaries the encoded feature into hashing code. However, the relative location information in the image may be lost when an image is encoded by the convolutional network, which makes it challenging to model the relationship of different modalities. Moreover, it is NP-hard to optimize the model with the discrete sign binary function popularly used in existing solutions. To address these issues, we propose a differentiable cross-modal hashing method that utilizes the multimodal transformer as the backbone to capture the location information in an image when encoding the visual content. In addition, a novel differentiable cross-modal hashing method is proposed to generate the binary code by a selecting mechanism, which could be formulated as a continuous and easily optimized problem. We perform extensive experiments on several cross modal datasets and the results show that the proposed method outperforms many existing solutions.