  This paper studies the problem of zero-shot sketch-based image retrieval
(ZS-SBIR), which aims to use sketches from unseen categories as queries to
match the images of the same category. Due to the large cross-modality
discrepancy, ZS-SBIR is still a challenging task and mimics realistic zero-shot
scenarios. The key is to leverage transferable knowledge from the pre-trained
model to improve generalizability. Existing researchers often utilize the
simple fine-tuning training strategy or knowledge distillation from a teacher
model with fixed parameters, lacking efficient bidirectional knowledge
alignment between student and teacher models simultaneously for better
generalization. In this paper, we propose a novel Symmetrical Bidirectional
Knowledge Alignment for zero-shot sketch-based image retrieval (SBKA). The
symmetrical bidirectional knowledge alignment learning framework is designed to
effectively learn mutual rich discriminative information between teacher and
student models to achieve the goal of knowledge alignment. Instead of the
former one-to-one cross-modality matching in the testing stage, a one-to-many
cluster cross-modality matching method is proposed to leverage the inherent
relationship of intra-class images to reduce the adverse effects of the
existing modality gap. Experiments on several representative ZS-SBIR datasets
(Sketchy Ext dataset, TU-Berlin Ext dataset and QuickDraw Ext dataset) prove
the proposed algorithm can achieve superior performance compared with
state-of-the-art methods.
