TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression

In this paper we investigate that the normalized coordinate expression is a key factor as reliance on hand-crafted components in query-based detectors for temporal action detection (TAD). Despite significant advancements towards an end-to-end framework in object detection query-based detectors have been limited in achieving full end-to-end modeling in TAD. To address this issue we propose TE-TAD a full end-to-end temporal action detection transformer that integrates time-aligned coordinate expression. We reformulate coordinate expression utilizing actual timeline values ensuring length-invariant representations from the extremely diverse video duration environment. Furthermore our proposed adaptive query selection dynamically adjusts the number of queries based on video length providing a suitable solution for varying video durations compared to a fixed query set. Our approach not only simplifies the TAD process by eliminating the need for hand-crafted components but also significantly improves the performance of query-based detectors. Our TE-TAD outperforms the previous query-based detectors and achieves competitive performance compared to state-of-the-art methods on popular benchmark datasets. Code is available at: https://github.com/Dotori-HJ/TE-TAD.