PGVT: Pose-Guided Video Transformer for Fine-Grained Action Recognition

Haosong Zhang, Mei Chee Leong, Liyuan Li, Weisi Lin; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 6645-6656

Based on recent advancements in transformer-based video models and multi-modal joint learning, we propose a novel model, named Pose-Guided Video Transformer (PGVT), to incorporate sparse high-level body joints locations and dense low-level visual pixels for effective learning and accurate recognition of human actions. PGVT leverages the pre-trained image models by freezing their parameters and introducing trainable adapters to effectively integrate two input modalities, i.e., human poses and video frames, to learn a pose-focused spatiotemporal representation of human actions. We design two novel core modules, i.e., Pose Temporal Attention and Pose-Video Spatial Attention, to facilitate interaction between body joint locations and uniform video tokens, enriching each modality with contextualized information from the other. We evaluate PGVT model on four action recognition datasets: Diving48, Gym99, and Gym288 for fine-grained action recognition, and Kinetics400 for coarse-grained action recognition. Our model achieves new SOTA performance on the three fine-grained human action recognition datasets and comparable performance on Kinetics400 with a small number of tunable parameters compared with SOTA methods. The PGVT model exploits effective multi-modality learning by explicitly modeling human body joints and leveraging their contextualized interactions with video clips.