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

arXiv: https://arxiv.org/abs/2403.19225 · HTML: https://mlsanity.com/paper/2403.19225/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 51 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [Cross-task weakly supervised learning from instructional videos](https://mlsanity.com/paper/1903.08225/) (2019)
- [ASFormer: Transformer for Action Segmentation](https://mlsanity.com/paper/2110.08568/) (2021)
- [All Tokens Matter: Token Labeling for Training Better Vision Transformers](https://mlsanity.com/paper/2104.10858/) (2021)
- [Multi-class Token Transformer for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.02891/) (2022)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly Supervised Action Alignment and Segmentation](https://mlsanity.com/paper/1901.02598/) (2019)
- [Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment](https://mlsanity.com/paper/1803.10699/) (2018)
- [Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation](https://mlsanity.com/paper/2003.02824/) (2020)
- [Diffusion Action Segmentation](https://mlsanity.com/paper/2303.17959/) (2023)
- [Connectionist Temporal Modeling for Weakly Supervised Action Labeling](https://mlsanity.com/paper/1607.08584/) (2016)
- [Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling](https://mlsanity.com/paper/1703.08132/) (2017)
- [NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning](https://mlsanity.com/paper/1805.06875/) (2018)
- [Weakly Supervised Energy-Based Learning for Action Segmentation](https://mlsanity.com/paper/1909.13155/) (2019)
- [Weakly Supervised Action Labeling in Videos Under Ordering Constraints](https://mlsanity.com/paper/1407.1208/) (2014)
- [Weakly supervised learning of actions from transcripts](https://mlsanity.com/paper/1610.02237/) (2016)
- [Global2Local: Efficient Structure Search for Video Action Segmentation](https://mlsanity.com/paper/2101.00910/) (2021)
- [A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation](https://mlsanity.com/paper/1906.01028/) (2019)
- [How Much Temporal Long-Term Context is Needed for Action Segmentation?](https://mlsanity.com/paper/2308.11358/) (2023)
- [Fast Weakly Supervised Action Segmentation Using Mutual Consistency](https://mlsanity.com/paper/1904.03116/) (2019)
- [Learning Discriminative Prototypes with Dynamic Time Warping](https://mlsanity.com/paper/2103.09458/) (2021)
- [UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection](https://mlsanity.com/paper/2111.14799/) (2021)

## Built on by (most cited first, 13 of 13)

- [Causal Temporal Representation Learning with Nonstationary Sparse Transition](https://mlsanity.com/paper/2409.03142/) (2024)
- [CLOT: Closed Loop Optimal Transport for Unsupervised Action Segmentation](https://mlsanity.com/paper/2507.03539/) (2025)
- [DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos](https://mlsanity.com/paper/2505.16376/) (2025)
- [2by2: Weakly-Supervised Learning for Global Action Segmentation](https://mlsanity.com/paper/2412.12829/) (2024)
- [iMiGUE-3K: A Large-Scale Benchmark for Micro-Gesture Analysis with Self-Supervised Learning](https://mlsanity.com/paper/2605.17179/) (2026)
- [Improving action segmentation via explicit similarity measurement](https://mlsanity.com/paper/2502.10713/) (2025)
- [From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in Industrial Settings](https://mlsanity.com/paper/2511.21428/) (2025)
- [Hierarchical Action Learning for Weakly-Supervised Action Segmentation](https://mlsanity.com/paper/2602.24275/) (2026)
- [Infant Cry Detection Using Causal Temporal Representation](https://mlsanity.com/paper/2503.06247/) (2025)
- [Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities](https://mlsanity.com/paper/2511.14945/) (2025)
- [D-CLOT: Double Closed Loop Optimal Transport for Unsupervised Action Segmentation](https://mlsanity.com/paper/2608.05877/) (2026)
- [VidParse: Online Parsing of Egocentric Procedures Like a Pro](https://mlsanity.com/paper/2608.27562/) (2026)
- [See the Change, Keep the Flow: Unsupervised Action Segmentation via Spectral-Temporal Representation Learning](https://mlsanity.com/paper/2608.29611/) (2026)

## Abstract (arXiv)

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.