OpenKD: Opening Prompt Diversity for Zero- and Few-Shot Keypoint Detection

Exploiting foundation models (e.g., CLIP) to build a versatile keypoint detector has gained increasing attention. Most existing models accept either the text prompt (e.g., “the nose of a cat”), or the visual prompt (e.g., support image with keypoint annotations), to detect the corresponding keypoints in query image, thereby, exhibiting either zero-shot or few-shot detection ability. However, the research on multimodal prompting is still underexplored, and the prompt diversity in semantics and language is far from opened. For example, how to handle unseen text prompts for novel keypoint detection and the diverse text prompts like “Can you detect the nose and ears of a cat?” In this work, we open the prompt diversity in three aspects: modality, semantics (seen vs. unseen), and language, to enable a more general zero- and few-shot keypoint detection (Z-FSKD). We propose a novel OpenKD model which leverages a multimodal prototype set to support both visual and textual prompting. Further, to infer the keypoint location of unseen texts, we add the auxiliary keypoints and texts interpolated in visual and textual domains into training, which improves the spatial reasoning of our model and significantly enhances zero-shot novel keypoint detection. We also find large language model (LLM) is a good parser, which achieves over 96% accuracy when parsing keypoints from texts. With LLM, OpenKD can handle diverse text prompts. Experimental results show that our method achieves state-of-the-art performance on Z-FSKD and initiates new ways of dealing with unseen text and diverse texts. The source code and data are available at https://github.com/AlanLuSun/OpenKD.