Weakly-supervised action segmentation is a task of learning to partition a
long video into several action segments, where training videos are only
accompanied by transcripts (ordered list of actions). Most of existing methods
need to infer pseudo segmentation for training by serial alignment between all
frames and the transcript, which is time-consuming and hard to be parallelized
while training. In this work, we aim to escape from this inefficient alignment
with massive but redundant frames, and instead to directly localize a few
action transitions for pseudo segmentation generation, where a transition
refers to the change from an action segment to its next adjacent one in the
transcript. As the true transitions are submerged in noisy boundaries due to
intra-segment visual variation, we propose a novel Action-Transition-Aware
Boundary Alignment (ATBA) framework to efficiently and effectively filter out
noisy boundaries and detect transitions. In addition, to boost the semantic
learning in the case that noise is inevitably present in the pseudo
segmentation, we also introduce video-level losses to utilize the trusted
video-level supervision. Extensive experiments show the effectiveness of our
approach on both performance and training speed.

本研究提出了一种新的弱监督动作分割方法，通过直接定位几个动作转换来生成伪分割，并利用边界对齐和视频级别监督来有效地过滤噪声并检测转换。实验证明了这种方法在性能和训练速度上的有效性。

基于动作 - 过渡感知边界对齐的高效有效的弱监督动作分割

Efficient and Effective Weakly-Supervised Action Segmentation via  Action-Transition-Aware Boundary Alignment

This paper proposes a novel active boundary loss for semantic segmentation.
It can progressively encourage the alignment between predicted boundaries and
ground-truth boundaries during end-to-end training, which is not explicitly
enforced in commonly used cross-entropy loss. Based on the predicted boundaries
detected from the segmentation results using current network parameters, we
formulate the boundary alignment problem as a differentiable direction vector
prediction problem to guide the movement of predicted boundaries in each
iteration. Our loss is model-agnostic and can be plugged in to the training of
segmentation networks to improve the boundary details. Experimental results
show that training with the active boundary loss can effectively improve the
boundary F-score and mean Intersection-over-Union on challenging image and
video object segmentation datasets.

本文提出了一种新的主动边界损失函数，通过不断引导预测边界和真实边界的对齐来改善语义分割效果，并且适用于图像和视频对象分割等复杂场景。