Cross-Modal Visual Correspondences Learning Without External Semantic Information for Zero-Shot Sketch-Based Image Retrieval

In this paper, we study the problem of zero-shot sketch-based image retrieval (ZS-SBIR), which is challenging because of the modal gap between sketch and image and the semantic inconsistency between seen categories and unseen categories. Most of the previous methods in ZS-SBIR, need external semantic information, i.e., texts and class labels, to minimize modal gap or semantic inconsistency. To tackle the challenging ZS-SBIR without external semantic information which is labor intensive, we propose a novel method of learning the visual correspondences between different modalities, i.e., sketch and image, to transfer knowledge from seen data to unseen data. This method is based on a transformer-based dual-pathway structure to learn the visual correspondences. In order to eliminate the modal gap between sketch and image, triplet loss and Gaussian distribution based domain alignment mechanism are introduced and performed on tokens obtained from our proposed structure. In addition, knowledge distillation is introduced to maintain the generalization capability brought by the vision transformer (ViT) used as the backbone to build the model. The comprehensive experiments on three benchmark datasets, i.e., Sketchy, TU-Berlin and QuickDraw, demonstrate that our method achieves superior results compared to baselines on all three datasets without external semantic information.