# GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos

arXiv: https://arxiv.org/abs/2307.11081 · HTML: https://mlsanity.com/paper/2307.11081/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks](https://mlsanity.com/paper/2003.10751/) (2020)
- [Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer](https://mlsanity.com/paper/2103.09712/) (2021)
- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [Video-based surgical skill assessment using 3D convolutional neural networks](https://mlsanity.com/paper/1903.02306/) (2019)
- [DeepPhase: Surgical Phase Recognition in CATARACTS Videos](https://mlsanity.com/paper/1807.10565/) (2018)
- [Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video](https://mlsanity.com/paper/1907.11454/) (2019)
- [Offline identification of surgical deviations in laparoscopic rectopexy](https://mlsanity.com/paper/1909.10790/) (2019)
- [Symmetric Dilated Convolution for Surgical Gesture Recognition](https://mlsanity.com/paper/2007.06373/) (2020)

## Built on by (most cited first, 5 of 5)

- [Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures](https://mlsanity.com/paper/2307.15220/) (2023)
- [SANGRIA: Surgical Video Scene Graph Optimization for Surgical Workflow Prediction](https://mlsanity.com/paper/2407.20214/) (2024)
- [SurgPETL: Parameter-Efficient Image-to-Surgical-Video Transfer Learning for Surgical Phase Recognition](https://mlsanity.com/paper/2409.20083/) (2024)
- [AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument Segmentation](https://mlsanity.com/paper/2411.03695/) (2024)
- [StepAL: Step-aware Active Learning for Cataract Surgical Videos](https://mlsanity.com/paper/2507.22059/) (2025)

## Abstract (arXiv)

Automated surgical step recognition is an important task that can significantly improve patient safety and decision-making during surgeries. Existing state-of-the-art methods for surgical step recognition either rely on separate, multi-stage modeling of spatial and temporal information or operate on short-range temporal resolution when learned jointly. However, the benefits of joint modeling of spatio-temporal features and long-range information are not taken in account. In this paper, we propose a vision transformer-based approach to jointly learn spatio-temporal features directly from sequence of frame-level patches. Our method incorporates a gated-temporal attention mechanism that intelligently combines short-term and long-term spatio-temporal feature representations. We extensively evaluate our approach on two cataract surgery video datasets, namely Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods. These results validate the suitability of our proposed approach for automated surgical step recognition. Our code is released at: https://github.com/nisargshah1999/GLSFormer