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1st Place Solutions for UG2+ Challenge 2021 -- (Semi-)supervised Face detection in the low light condition

arXiv 2107.00818 ↗First posted 2 Jul 2021cs.CVcited by 0

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Abstract (arXiv)

In this technical report, we briefly introduce the solution of our team "TAL-ai" for (Semi-) supervised Face detection in the low light condition in UG2+ Challenge in CVPR 2021. By conducting several experiments with popular image enhancement methods and image transfer methods, we pulled the low light image and the normal image to a more closer domain. And it is observed that using these data to training can achieve better performance. We also adapt several popular object detection frameworks, e.g., DetectoRS, Cascade-RCNN, and large backbone like Swin-transformer. Finally, we ensemble several models which achieved mAP 74.89 on the testing set, ranking 1st on the final leaderboard.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 2 Jul 2021.

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References · 14

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2 more
  1. 2021HLA-Face: Joint High-Low Adaptation for Low Light Face Detectioncited by 28
  2. 2018Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Resultscited by 16

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