# DACAT: Dual-stream Adaptive Clip-aware Time Modeling for Robust Online Surgical Phase Recognition

arXiv: https://arxiv.org/abs/2409.06217 · HTML: https://mlsanity.com/paper/2409.06217/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Medical SAM 2: Segment medical images as video via Segment Anything Model 2](https://mlsanity.com/paper/2408.00874/) (2024)
- [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)
- [AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy](https://mlsanity.com/paper/2208.02049/) (2022)
- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [OperA: Attention-Regularized Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2103.03873/) (2021)
- [LoViT: Long Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2305.08989/) (2023)
- [Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search](https://mlsanity.com/paper/2002.08718/) (2020)
- [On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis](https://mlsanity.com/paper/2203.07976/) (2022)
- [Less is More: Surgical Phase Recognition from Timestamp Supervision](https://mlsanity.com/paper/2202.08199/) (2022)

## Built on by (most cited first, 3 of 3)

- [First-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher Network](https://mlsanity.com/paper/2412.16503/) (2024)
- [CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition](https://mlsanity.com/paper/2503.10216/) (2025)
- [SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition](https://mlsanity.com/paper/2605.14889/) (2026)

## Abstract (arXiv)

Surgical phase recognition has become a crucial requirement in laparoscopic surgery, enabling various clinical applications like surgical risk forecasting. Current methods typically identify the surgical phase using individual frame-wise embeddings as the fundamental unit for time modeling. However, this approach is overly sensitive to current observations, often resulting in discontinuous and erroneous predictions within a complete surgical phase. In this paper, we propose DACAT, a novel dual-stream model that adaptively learns clip-aware context information to enhance the temporal relationship. In one stream, DACAT pretrains a frame encoder, caching all historical frame-wise features. In the other stream, DACAT fine-tunes a new frame encoder to extract the frame-wise feature at the current moment. Additionally, a max clip-response read-out (Max-R) module is introduced to bridge the two streams by using the current frame-wise feature to adaptively fetch the most relevant past clip from the feature cache. The clip-aware context feature is then encoded via cross-attention between the current frame and its fetched adaptive clip, and further utilized to enhance the time modeling for accurate online surgical phase recognition. The benchmark results on three public datasets, i.e., Cholec80, M2CAI16, and AutoLaparo, demonstrate the superiority of our proposed DACAT over existing state-of-the-art methods, with improvements in Jaccard scores of at least 4.5%, 4.6%, and 2.7%, respectively. Our code and models have been released at https://github.com/kk42yy/DACAT.