Enhancing Robust Category‐Agnostic Pose Estimation through Multi‐Modal Feature Alignment

Category‐Agnostic Pose Estimation (CAPE) aims to detect keypoints for objects of any category using only a few labeled samples, making it a challenging yet crucial task for general‐purpose visual understanding. Existing methods rely on either visual or textual inputs, but the lack of cross‐modal interaction limits generalization. Without a unified input representation, solely using visual features hinders consistent prediction of same‐type keypoints, while fixed textual representations fail to capture the diverse characteristics of same‐type keypoints, leading to coarse and over‐generalized outputs. To address these limitations, we propose two multi‐modal frameworks that perform visual‐textual integration at both the feature and decision levels. Our feature‐level module leverages cross‐modal attention to align and enhance keypoint representations, while the decision‐level fusion adaptively combines modality‐specific predictions through a modality‐consistency loss. Experiments on the large‐scale MP‐100 dataset demonstrate that our method surpasses existing baselines in both accuracy and robustness. Under the challenging 1‐shot setting, our model achieves a 0.58% improvement in PCK0.2 over the state‐of‐the‐art CAPE method.