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.11Code is available at https://github.com/iSEE-Laboratory/CVPR24_ATBA.

Long-term action quality assessment is a task of evaluating how well an action is performed, namely, estimating a quality score from a long video. Intuitively, long-term actions generally involve parts exhibiting different levels of skill, and we call the levels of skill as performance grades. For example, technical highlights and faults may appear in the same long-term action. Hence, the final score should be determined by the comprehensive effect of different grades exhibited in the video. To explore this latent relationship, we design a novel Likert scoring paradigm in-spired by the Likert scale in psychometrics, in which we quantify the grades explicitly and generate the final quality score by combining the quantitative values and the corresponding responses estimated from the video, instead of performing direct regression. Moreover, we extract grade-specific features, which will be used to estimate the responses of each grade, through a Transformer decoder architecture with diverse learnable queries. The whole model is named as Grade-decoupling Likert Transformer (GDLT), and we achieve state-of-the-art results on two long-term action assessment datasets.11Project page https://isee-ai.cn/-angchi/CVPR22_GDLT.html