AI看动作时，别让它只认物体
你让AI识别“打开抽屉”，它可能根本没看“打开”这个动作，而是看到“抽屉”就猜“打开”。这篇论文发现，当前最好的模型在识别动作时，会偷懒依赖物体标签（比如看到“杯子”就猜“倒水”），而不是真正理解动作的时间顺序。研究者提出了RCORE方法，通过两种手段纠正：一是把常见的物体-动作组合当作“反面教材”来训练，二是强制模型关注动作的时间顺序。在两大基准测试上，RCORE减少了这种偷懒行为，提升了识别新组合的准确率。它不是你明天能用上的，但揭示了AI视觉识别中的一个关键盲点。
📄 原文摘要(英文)
Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primitives. In this work, we tackle a key failure mode: models predict verbs via object-driven shortcuts (i.e., relying on the labeled object class) rather than temporal evidence. We argue that sparse compositional supervision and verb-object learning asymmetry can promote object-driven shortcut learning. Our analysis with proposed diagnostic metrics shows that existing methods overfit to training co-occurrence patterns and underuse temporal verb cues, resulting in weak generalization to unseen compositions. To address object-driven shortcuts, we propose Robust COmpositional REpresentations (RCORE) with two components. Co-occurrence Prior Regularization (CPR) adds explicit supervision for unseen compositions and regularizes the model against frequent co-occurrence priors by treating them as hard negatives. Temporal Order Regularization for Composition (TORC) enforces temporal-order sensitivity to learn temporally grounded verb representations. Across Sth-com and EK100-com, RCORE reduces shortcut diagnostics and consequently improves compositional generalization.