AutoLoc: Weakly Supervised Temporal Action Localization in Untrimmed Videos
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.
ECCV