Fully supervised action segmentation works on frame-wise action recognition
with dense annotations and often suffers from the over-segmentation issue.
Existing works have proposed a variety of solutions such as boundary-aware
networks, multi-stage refinement, and temporal smoothness losses. However, most
of them take advantage of frame-wise supervision, which cannot effectively
tackle the evaluation metrics with different granularities. In this paper, for
the desirable large receptive field, we first develop a novel local-global
attention mechanism with temporal pyramid dilation and temporal pyramid pooling
for efficient multi-scale attention. Then we decouple two inherent goals in
action segmentation, ie, (1) individual identification solved by frame-wise
supervision, and (2) temporal reasoning tackled by action set prediction.
Afterward, an action alignment module fuses these different granularity
predictions, leading to more accurate and smoother action segmentation. We
achieve state-of-the-art accuracy, eg, 82.8% (+2.6%) on GTEA and 74.7% (+1.2%)
on Breakfast, which demonstrates the effectiveness of our proposed method,
accompanied by extensive ablation studies. The code will be made available
later.

本文提出了一种新颖的本地 - 全局注意力机制来解决行动分割中过度分割问题，同时通过时间金字塔扩张和池化来实现高效多尺度关注，将个体识别与时间推理解耦，并通过行动对齐模块来融合不同的粒度级别预测，从而实现更精确和更平滑的行动分割， 在 GTEA 和 Breakfast 数据集上取得了最先进的准确性。

DIR-AS: 将个体识别和时间推理分离以进行动作分割

DIR-AS: Decoupling Individual Identification and Temporal Reasoning for  Action Segmentation

In this paper, we describe a novel approach to imitation learning that infers
latent policies directly from state observations. We introduce a method that
characterizes the causal effects of latent actions on observations while
simultaneously predicting their likelihood. We then outline an action alignment
procedure that leverages a small amount of environment interactions to
determine a mapping between the latent and real-world actions. We show that
this corrected labeling can be used for imitating the observed behavior, even
though no expert actions are given. We evaluate our approach within classic
control environments and a platform game and demonstrate that it performs
better than standard approaches. Code for this work is available at
this https URL

本文提出了一种新的模仿学习方法，直接从状态观测推断潜在策略，并引入了一种方法来描述潜在动作对观测的因果影响，同时预测它们的可能性，从而确定潜在和实际行为之间的映射。本文在经典控制环境和平台游戏中评估了该方法，并表明它的性能优于标准方法。

从观察中模仿潜在策略

Imitating Latent Policies from Observation

We present an approach for weakly supervised learning of human actions. Given
a set of videos and an ordered list of the occurring actions, the goal is to
infer start and end frames of the related action classes within the video and
to train the respective action classifiers without any need for hand labeled
frame boundaries. To address this task, we propose a combination of a
discriminative representation of subactions, modeled by a recurrent neural
network, and a coarse probabilistic model to allow for a temporal alignment and
inference over long sequences. While this system alone already generates good
results, we show that the performance can be further improved by approximating
the number of subactions to the characteristics of the different action
classes. To this end, we adapt the number of subaction classes by iterating
realignment and reestimation during training. The proposed system is evaluated
on two benchmark datasets, the Breakfast and the Hollywood extended dataset,
showing a competitive performance on various weak learning tasks such as
temporal action segmentation and action alignment.

本文提出了一种弱监督学习的方法，结合判别子动作的表示和粗略概率模型，应用于人类行为的动作检测和分类任务中，并在两个基准数据集上进行了评估，展现了在多个弱监督学习任务中的良好性能。