← 研踪 PaperTrailCVF · CVPR 2026:cvf:708e9ef879b0ed72afa99f38bf5d5839 · 记录 #1
Bridging the Modality Gap in Compositional Zero-Shot Learning via Sparse Alignment and Unimodal Memory Bank
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:01
摘要
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions with learned primitives (attribute and object) knowledge from seen compositions. While previous approaches gain their notable performance through the powerful cross-modal alignment of CLIP, they often overlook the modality gap, an inherent constraint stemming from information-imbalanced training data. In this work, we propose SAM, a novel \underline \text S parse \underline \text A lignment and Unimoal \underline \text M emory Bank to effectively bridging modality gap for CZSL. Specifically, we conduct sparse alignment that links textual representations directly to their semantically pertinent visual patches. This direct linking serves to prune redundant visual data and counter the information imbalance in image-text pairs. Subsequently, with the sparsely aligned visual information as its guidance, the visual adaptive condensation module adaptively fuses these critical cues into a unified representation. Finally, we introduce a dynamically updated memory bank that stores samples from both seen and unseen compositions. This bank serves a dual purpose: it bypasses the modality gap through visual-only classification and concurrently strengthens generalization to unseen compositions. Experiments on three benchmarks demonstrate that our method gains significant improvements over CLIP-based methods under closed-world and open-world settings.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
Bridging the Modality Gap in Compositional Zero-Shot Learning via Sparse Alignment and Unimodal Memory Bank
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions with learned primitives (attribute and object) knowledge from seen compositions. While previous approaches gain their notable performance through the powerful cross-modal alignment of CLIP, they often overlook the modality gap, an inherent constraint stemming from information-imbalanced training data. In this work, we propose SAM, a novel \underline \text S parse \underline \text A lignment and Unimoal \underline \text M emory Bank to effectively bridging modality gap for CZSL. Specifically, we conduct sparse alignment that links textual representations directly to their semantically pertinent visual patches. This direct linking serves to prune redundant visual data and counter the information imbalance in image-text pairs. Subsequently, with the sparsely aligned visual information as its guidance, the visual adaptive condensation module adaptively fuses these critical cues into a unified representation. Finally, we introduce a dynamically updated memory bank that stores samples from both seen and unseen compositions. This bank serves a dual purpose: it bypasses the modality gap through visual-only classification and concurrently strengthens generalization to unseen compositions. Experiments on three benchmarks demonstrate that our method gains significant improvements over CLIP-based methods under closed-world and open-world settings.
查看此版本解读 →查看此版本原文 ↗