TOMCAT: Test-time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning
This addresses a key challenge in recognizing novel attribute-object compositions for AI systems, though it is incremental as it builds on existing CZSL methods.
The paper tackles performance degradation in Compositional Zero-Shot Learning caused by distribution shifts at test time by accumulating comprehensive knowledge from unsupervised data to update multimodal prototypes, achieving state-of-the-art results on four benchmark datasets.
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions based on the knowledge learned from seen ones. Existing methods suffer from performance degradation caused by the distribution shift of label space at test time, which stems from the inclusion of unseen compositions recombined from attributes and objects. To overcome the challenge, we propose a novel approach that accumulates comprehensive knowledge in both textual and visual modalities from unsupervised data to update multimodal prototypes at test time. Building on this, we further design an adaptive update weight to control the degree of prototype adjustment, enabling the model to flexibly adapt to distribution shift during testing. Moreover, a dynamic priority queue is introduced that stores high-confidence images to acquire visual knowledge from historical images for inference. Considering the semantic consistency of multimodal knowledge, we align textual and visual prototypes by multimodal collaborative representation learning. Extensive experiments indicate that our approach achieves state-of-the-art performance on four benchmark datasets under both closed-world and open-world settings. Code will be available at https://github.com/xud-yan/TOMCAT .