Weakly Supervised Temporal Action Localization (WTAL) aims to classify and
localize temporal boundaries of actions for the video, given only video-level
category labels in the training datasets. Due to the lack of boundary
information during training, existing approaches formulate WTAL as a
classificationproblem, i.e., generating the temporal class activation map
(T-CAM) for localization. However, with only classification loss, the model
would be sub-optimized, i.e., the action-related scenes are enough to
distinguish different class labels. Regarding other actions in the
action-related scene ( i.e., the scene same as positive actions) as co-scene
actions, this sub-optimized model would misclassify the co-scene actions as
positive actions. To address this misclassification, we propose a simple yet
efficient method, named bidirectional semantic consistency constraint (Bi-SCC),
to discriminate the positive actions from co-scene actions. The proposed Bi-SCC
firstly adopts a temporal context augmentation to generate an augmented video
that breaks the correlation between positive actions and their co-scene actions
in the inter-video; Then, a semantic consistency constraint (SCC) is used to
enforce the predictions of the original video and augmented video to be
consistent, hence suppressing the co-scene actions. However, we find that this
augmented video would destroy the original temporal context. Simply applying
the consistency constraint would affect the completeness of localized positive
actions. Hence, we boost the SCC in a bidirectional way to suppress co-scene
actions while ensuring the integrity of positive actions, by cross-supervising
the original and augmented videos. Finally, our proposed Bi-SCC can be applied
to current WTAL approaches, and improve their performance. Experimental results
show that our approach outperforms the state-of-the-art methods on THUMOS14 and
ActivityNet.

该研究提出了一种名为双向语义一致性约束的方法来改善当前弱监督下的视频分类及定位发现问题。该方法采用时间相关增强来打破正样本动作与其 co-scene 动作之间的相关性，通过语义一致性约束来获得足够的信息区分两者，从而较好地应用于当前的 WTAL 方法，并且在 THUMOS14 和 ActivityNet 数据集上取得了较好的性能。