SALAD: Self-Assessment Learning for Action Detection

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.