3.8 Proceedings Paper

SALAD: Self-Assessment Learning for Action Detection

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/WACV48630.2021.00131

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Funding

  1. GENCI-IDRIS [2019-AD011011269]

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Research shows that learning self-assessment scores in action detection can improve overall performance, with experimental results demonstrating that the approach outperforms the state-of-the-art on two action detection benchmarks.
Literature on self-assessment in machine learning mainly focuses on the production of well-calibrated algorithms through consensus frameworks i.e. calibration is seen as a problem. Yet, we observe that learning to be properly confident could behave like a powerful regularization and thus, could be an opportunity to improve performance. Precisely, we show that used within a framework of action detection, the learning of a self-assessment score is able to improve the whole action localization process. Experimental results show that our approach outperforms the state-of-the-art on two action detection benchmarks. On THUMOS14 dataset, the mAP at tIoU@0:5 is improved from 42:8% to 44:6%, and from 50:4% to 51:7% on ActivityNet1.3 dataset. For lower tIoU values, we achieve even more significant improvements on both datasets.

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