  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<EMAIL_ADDRESS>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.
