Prediction-Feedback DETR for Temporal Action Detection
<|reference_start|>Prediction-Feedback DETR for Temporal Action Detection: Temporal Action Detection (TAD) is fundamental yet challenging for real-world video applications. Leveraging the unique benefits of transformers, various DETR-based approaches have been adopted in TAD. However, it has recently been identified that the attention collapse in self-attention causes the performance degradation of DETR for TAD. Building upon previous research, this paper newly addresses the attention collapse problem in cross-attention within DETR-based TAD methods. Moreover, our findings reveal that cross-attention exhibits patterns distinct from predictions, indicating a short-cut phenomenon. To resolve this, we propose a new framework, Prediction-Feedback DETR (Pred-DETR), which utilizes predictions to restore the collapse and align the cross- and self-attention with predictions. Specifically, we devise novel prediction-feedback objectives using guidance from the relations of the predictions. As a result, Pred-DETR significantly alleviates the collapse and achieves state-of-the-art performance among DETR-based methods on various challenging benchmarks including THUMOS14, ActivityNet-v1.3, HACS, and FineAction.<|reference_end|>
@article{kim2024prediction-feedback,
    title={Prediction-Feedback DETR for Temporal Action Detection},
    author={Jihwan Kim, Miso Lee, Cheol-Ho Cho, Jihyun Lee, Jae-Pil Heo},
    journal={arXiv preprint arXiv:2408.16729},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16729},
    primaryClass={cs.CV}
}