论文标题
沙拉：动作检测的自我评估学习
SALAD: Self-Assessment Learning for Action Detection
论文作者
论文摘要
关于机器学习中自我评估的文献主要集中在通过共识框架（即校准）视为问题的算法算法的生产。然而，我们观察到，学会正确自信可能会像一个强大的正规化一样，因此，可能是提高性能的机会。首先，我们表明，在动作检测框架内使用的是，学习自我评估得分的学习能够改善整个动作定位过程。实验性结果表明，我们的方法表明，我们的方法在两个动作检测方面均优于两个动作探测器。在Thumos14数据集上，[email protected]的地图从42.8 \％提高到44.6 \％，从50.4 \％\％\％\％\％\％\％\％\％\％。对于较低的TIOU值，我们在两个数据集上都取得了更大的改进。
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 protected] 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.
