Keypoint-based action recognition offers robustness to appearance variations and provides privacy-preserving representation.However, existing zero-shot (ZS) approaches largely emphasize human motion while underutilizing contextual information, particularly human-object interactions.Moreover, extending keypoint-based ZS models to few-shot scenarios remains insufficiently explored.We propose Instance-aware Semantic Alignment and Transfer (InsAT), a unified framework for ZS recognition and zero-to-few-shot (Z2F) adaptation that leverages instance-level language descriptions.InsAT aligns textual descriptions of humans, objects, and their interactions with visual representations derived from human and object keypoints, enabling effective transfer of interaction knowledge from seen to unseen action classes.To support Z2F adaptation, we introduce Instance-level Visual Adaptation, a parameter-free mechanism that improves recognition by incorporating instance-level contextual cues without updating model weights.Extensive experiments demonstrate that InsAT substantially outperforms prior keypoint-based ZS methods and achieves competitive performance relative to large vision-language models, while remaining data-efficient and robust.

In this paper, we propose a method that addresses two challenges faced by existing generalized few-shot object detection methods, limiting their scalability in practical applications: (i) the high training cost required for few-shot learning of a small number of target object samples, and (ii) the lack of variation in these samples. Leveraging the generalization ability of a deep neural network (DNN) trained with vision-language pretraining, we introduce a frozen network few-shot object detection framework in which a common DNN, shared with the pretraining phase, is used to model the appearance features of target objects without needing to be updated during few-shot learning. In experiments, we employed publicly available datasets with diverse object appearances for pretraining and few-shot learning. By comparing our proposed method’s detection accuracy with existing methods, we demonstrate that our method effectively addresses the challenges described above.