Temporal Action Localization (TAL) in untrimmed video is important for many
applications. But it is very expensive to annotate the segment-level ground
truth (action class and temporal boundary). This raises the interest of
addressing TAL with weak supervision, namely only video-level annotations are
available during training). However, the state-of-the-art weakly-supervised TAL
methods only focus on generating good Class Activation Sequence (CAS) over time
but conduct simple thresholding on CAS to localize actions. In this paper, we
first develop a novel weakly-supervised TAL framework called AutoLoc to
directly predict the temporal boundary of each action instance. We propose a
novel Outer-Inner-Contrastive (OIC) loss to automatically discover the needed
segment-level supervision for training such a boundary predictor. Our method
achieves dramatically improved performance: under the IoU threshold 0.5, our
method improves mAP on THUMOS'14 from 13.7% to 21.2% and mAP on ActivityNet
from 7.4% to 27.3%. It is also very encouraging to see that our
weakly-supervised method achieves comparable results with some fully-supervised
methods.

本文提出了一种基于弱监督的时序动作定位框架 AutoLoc, 运用 Outer-Inner-Contrastive (OIC) 损失函数，通过自动发现分段级别监督训练出定位模型，模型在 THUMOS'14 数据集中，IoU 阈值为 0.5 时，平均精度从 13.7% 提高至 21.2%，在 ActivityNet 数据集中，平均精度从 7.4% 提高至 27.3%。