Weakly-supervised Temporal Action Localization (WS-TAL) methods learn to
localize temporal starts and ends of action instances in a video under only
video-level supervision. Existing WS-TAL methods rely on deep features learned
for action recognition. However, due to the mismatch between classification and
localization, these features cannot distinguish the frequently co-occurring
contextual background, i.e., the context, and the actual action instances. We
term this challenge action-context confusion, and it will adversely affect the
action localization accuracy. To address this challenge, we introduce a
framework that learns two feature subspaces respectively for actions and their
context. By explicitly accounting for action visual elements, the action
instances can be localized more precisely without the distraction from the
context. To facilitate the learning of these two feature subspaces with only
video-level categorical labels, we leverage the predictions from both spatial
and temporal streams for snippets grouping. In addition, an unsupervised
learning task is introduced to make the proposed module focus on mining
temporal information. The proposed approach outperforms state-of-the-art WS-TAL
methods on three benchmarks, i.e., THUMOS14, ActivityNet v1.2 and v1.3
datasets.

通过学习两个特征子空间（分别专门用于行为和背景）并利用时空运动预测来促进两个特征子空间的学习，本文提出了一种解决弱监督时间动作本地化的方法，该方法在 THUMOS14、ActivityNet v1.2 和 v1.3 数据集上优于现有的 WS-TAL 方法。

通过学习明确的动作和情境子空间，实现弱监督的时间行为定位

Weakly Supervised Temporal Action Localization Through Learning Explicit  Subspaces for Action and Context

Weakly-supervised temporal action localization is a problem of learning an
action localization model with only video-level action labeling available. The
general framework largely relies on the classification activation, which
employs an attention model to identify the action-related frames and then
categorizes them into different classes. Such method results in the
action-context confusion issue: context frames near action clips tend to be
recognized as action frames themselves, since they are closely related to the
specific classes. To solve the problem, in this paper we propose to model the
class-agnostic frame-wise probability conditioned on the frame attention using
conditional Variational Auto-Encoder (VAE). With the observation that the
context exhibits notable difference from the action at representation level, a
probabilistic model, i.e., conditional VAE, is learned to model the likelihood
of each frame given the attention. By maximizing the conditional probability
with respect to the attention, the action and non-action frames are well
separated. Experiments on THUMOS14 and ActivityNet1.2 demonstrate advantage of
our method and effectiveness in handling action-context confusion problem. Code
is now available on GitHub.

本文提出了一种使用条件变分自编码器模型来处理弱监督下的动作定位问题的方法，通过建立一个概率模型来模拟每一帧在给定关注度的情况下应当属于动作或非动作中的哪一类，从而有效地解决了动作与上下文之间相互混淆的问题。