Weakly-supervised temporal action localization (WS-TAL) aims to localize
actions in untrimmed videos with only video-level labels. Most existing models
follow the "localization by classification" procedure: locate temporal regions
contributing most to the video-level classification. Generally, they process
each snippet (or frame) individually and thus overlook the fruitful temporal
context relation. Here arises the single snippet cheating issue: "hard"
snippets are too vague to be classified. In this paper, we argue that learning
by comparing helps identify these hard snippets and we propose to utilize
snippet Contrastive learning to Localize Actions, CoLA for short. Specifically,
we propose a Snippet Contrast (SniCo) Loss to refine the hard snippet
representation in feature space, which guides the network to perceive precise
temporal boundaries and avoid the temporal interval interruption. Besides,
since it is infeasible to access frame-level annotations, we introduce a Hard
Snippet Mining algorithm to locate the potential hard snippets. Substantial
analyses verify that this mining strategy efficaciously captures the hard
snippets and SniCo Loss leads to more informative feature representation.
Extensive experiments show that CoLA achieves state-of-the-art results on
THUMOS'14 and ActivityNet v1.2 datasets. CoLA code is publicly available at
this https URL

本文提出了一种基于 Snippet Contrastive learning 和 Hard Snippet Mining 的弱监督视频动作定位模型，弥补了传统模型单独处理每个片段的不足，使得模型能更准确地定位动作并取得了当前最先进的结果。