# End-to-End Action Segmentation Transformer

arXiv: https://arxiv.org/abs/2503.06316 · HTML: https://mlsanity.com/paper/2503.06316/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 28 of 28)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://mlsanity.com/paper/2303.16727/) (2023)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities](https://mlsanity.com/paper/2203.14712/) (2022)
- [ASFormer: Transformer for Action Segmentation](https://mlsanity.com/paper/2110.08568/) (2021)
- [Temporal Action Segmentation: An Analysis of Modern Techniques](https://mlsanity.com/paper/2210.10352/) (2022)
- [Diffusion Action Segmentation](https://mlsanity.com/paper/2303.17959/) (2023)
- [Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation](https://mlsanity.com/paper/2209.00638/) (2022)
- [Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos](https://mlsanity.com/paper/2203.14104/) (2022)
- [End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames](https://mlsanity.com/paper/2311.17241/) (2023)
- [How Much Temporal Long-Term Context is Needed for Action Segmentation?](https://mlsanity.com/paper/2308.11358/) (2023)
- [Action Shuffle Alternating Learning for Unsupervised Action Segmentation](https://mlsanity.com/paper/2104.02116/) (2021)
- [Coarse to Fine Multi-Resolution Temporal Convolutional Network](https://mlsanity.com/paper/2105.10859/) (2021)
- [MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/2006.09220/) (2020)
- [Efficient Temporal Action Segmentation via Boundary-aware Query Voting](https://mlsanity.com/paper/2405.15995/) (2024)

## Built on by (most cited first, 1 of 1)

- [Polyphony: Diffusion-based Dual-Hand Action Segmentation with Alternating Vision Transformer and Semantic Conditioning](https://mlsanity.com/paper/2605.31115/) (2026)

## Abstract (arXiv)

Most recent work on action segmentation relies on pre-computed frame features from models trained on other tasks and typically focuses on framewise encoding and labeling without explicitly modeling action segments. To overcome these limitations, we introduce the End-to-End Action Segmentation Transformer (EAST), which processes raw video frames directly -- eliminating the need for pre-extracted features and enabling true end-to-end training. Our contributions are as follows: (1) a lightweight adapter design for effective fine-tuning of large backbones; (2) an efficient segmentation-by-detection framework for leveraging action proposals predicted over a coarsely downsampled video; and (3) a novel action-proposal-based data augmentation strategy. EAST achieves SOTA performance on standard benchmarks, including GTEA, 50Salads, Breakfast, and Assembly-101.