CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose
This work addresses the problem of limited training data and high variability in animal pose estimation for researchers and practitioners in computer vision, introducing a novel cross-modal paradigm.
The paper tackles the challenge of animal pose estimation by proposing CLAMP, a prompt-based contrastive learning method that connects pre-trained language models with visual keypoints, achieving state-of-the-art performance in supervised, few-shot, and zero-shot settings with significant improvements over image-based methods.
Animal pose estimation is challenging for existing image-based methods because of limited training data and large intra- and inter-species variances. Motivated by the progress of visual-language research, we propose that pre-trained language models (e.g., CLIP) can facilitate animal pose estimation by providing rich prior knowledge for describing animal keypoints in text. However, we found that building effective connections between pre-trained language models and visual animal keypoints is non-trivial since the gap between text-based descriptions and keypoint-based visual features about animal pose can be significant. To address this issue, we introduce a novel prompt-based Contrastive learning scheme for connecting Language and AniMal Pose (CLAMP) effectively. The CLAMP attempts to bridge the gap by adapting the text prompts to the animal keypoints during network training. The adaptation is decomposed into spatial-aware and feature-aware processes, and two novel contrastive losses are devised correspondingly. In practice, the CLAMP enables the first cross-modal animal pose estimation paradigm. Experimental results show that our method achieves state-of-the-art performance under the supervised, few-shot, and zero-shot settings, outperforming image-based methods by a large margin.