Adapting Short-Term Transformers for Action Detection in Untrimmed Videos

Min Yang, Huan Gao, Ping Guo, Limin Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 18570-18579

Vision Transformer (ViT) has shown high potential in video recognition owing to its flexible design adaptable self-attention mechanisms and the efficacy of masked pre-training. Yet it remains unclear how to adapt these pre-trained short-term ViTs for temporal action detection (TAD) in untrimmed videos. The existing works treat them as off-the-shelf feature extractors for each short-trimmed snippet without capturing the fine-grained relation among different snippets in a broader temporal context. To mitigate this issue this paper focuses on designing a new mechanism for adapting these pre-trained ViT models as a unified long-form video transformer to fully unleash its modeling power in capturing inter-snippet relation while still keeping low computation overhead and memory consumption for efficient TAD. To this end we design effective cross-snippet propagation modules to gradually exchange short-term video information among different snippets from two levels. For inner-backbone information propagation we introduce a cross-snippet propagation strategy to enable multi-snippet temporal feature interaction inside the backbone. For post-backbone information propagation we propose temporal transformer layers for further clip-level modeling. With the plain ViT-B pre-trained with VideoMAE our end-to-end temporal action detector (ViT-TAD) yields a very competitive performance to previous temporal action detectors riching up to 69.5 average mAP on THUMOS14 37.40 average mAP on ActivityNet-1.3 and 17.20 average mAP on FineAction.