  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 \modelname{}, 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
