mlsanityData to 7 Oct 2026

One-stage Action Detection Transformer

arXiv 2206.10080 ↗First posted 21 Jun 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

In this work, we introduce our solution to the EPIC-KITCHENS-100 2022 Action Detection challenge. One-stage Action Detection Transformer (OADT) is proposed to model the temporal connection of video segments. With the help of OADT, both the category and time boundary can be recognized simultaneously. After ensembling multiple OADT models trained from different features, our model can reach 21.28% action mAP and ranks the 1st on the test-set of the Action detection challenge.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 23 references that are in the map. First posted on arXiv 21 Jun 2022.

Builds on

18 of 23 references.

References · 18

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6 more
  1. 2018Long-Term Feature Banks for Detailed Video Understandingcited by 239
  2. 2021Keeping Your Eye on the Ball: Trajectory Attention in Video Transformerscited by 224
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