面向组合式零样本学习的图引导跨组合特征解耦
Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning
AI总结：
针对组合式零样本学习中基元特征跨组合通用性难题，提出图引导跨组合特征解耦方法，在CLIP模型中引入双重解耦适配器，显著提升了模型性能。
摘要：
基元（即属性和物体）视觉特征的解耦在组合式零样本学习（CZSL）中展现出了卓越的结果。然而，由于某一属性（或物体）在与不同物体（或属性）组合时存在特征差异，学习在不同组合间具有通用性的解耦基元特征极具挑战。为此，我们提出跨组合特征解耦方案，将多个共享基元的组合作为输入，并约束解耦的基元特征在这些组合间具有通用性。具体而言，我们利用组合图来定义组合间的整体共享基元关系，并在近期成功的大型预训练视觉-语言模型（VLM）CLIP基础上构建任务特定架构，将双重跨组合解耦适配器（分别称为L-Adapter和V-Adapter）插入CLIP冻结的文本和图像编码器中。在三个流行的CZSL基准上的评估表明，我们提出的方案显著提升了CZSL的性能，且其各组件已通过扎实的消融研究得到验证。
英文摘要：
Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the feature divergence of an attribute (resp. object) when combined with different objects (resp. attributes), it is challenging to learn disentangled primitive features that are general across different compositions. To this end, we propose the solution of cross-composition feature disentanglement, which takes multiple primitive-sharing compositions as inputs and constrains the disentangled primitive features to be general across these compositions. More specifically, we leverage a compositional graph to define the overall primitive-sharing relationships between compositions, and build a task-specific architecture upon the recently successful large pre-trained vision-language model (VLM) CLIP, with dual cross-composition disentangling adapters (called L-Adapter and V-Adapter) inserted into CLIP's frozen text and image encoders, respectively. Evaluation on three popular CZSL benchmarks shows that our proposed solution significantly improves the performance of CZSL, and its components have been verified by solid ablation studies. Our code and data are available at:https://github.com/zhurunkai/DCDA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
