Weakly-Supervised Deep Image Hashing based on Cross-Modal Transformer

Abstract

Weakly-supervised image hashing emerges recently because web images associated with contextual text or tags are abundant. Text information weakly-related to images can be utilized to guide the learning of a deep hashing network. In this paper, we propose Weakly-supervised deep Hashing based on Cross-Modal Transformer (WHCMT). First, cross-scale attention between image patches is discovered to form more effective visual representations. A baseline transformer is also adopted to find self-attention of tags and form tag representations. Second, the cross-modal attention between images and tags is discovered by the proposed cross-modal transformer. Effective hash codes are then generated by embedding layers. WHCMT is tested on semantic image retrieval, and we show new state-of-the-art results can be obtained for the MIRFLICKR-25K dataset and NUS-WIDE dataset.