# Beyond Instrument Motion: Recognizing Tissue Tension Toward Surgical Skill Assessment

arXiv: https://arxiv.org/abs/2608.17935 · HTML: https://mlsanity.com/paper/2608.17935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 26 of 26)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [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)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [A Short Note about Kinetics-600](https://mlsanity.com/paper/1808.01340/) (2018)
- [2017 Robotic Instrument Segmentation Challenge](https://mlsanity.com/paper/1902.06426/) (2019)
- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos](https://mlsanity.com/paper/2410.11831/) (2024)
- [2018 Robotic Scene Segmentation Challenge](https://mlsanity.com/paper/2001.11190/) (2020)
- [AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy](https://mlsanity.com/paper/2208.02049/) (2022)
- [Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets](https://mlsanity.com/paper/2007.05405/) (2020)
- [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021)
- [SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge](https://mlsanity.com/paper/2401.00496/) (2023)
- [Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room](https://mlsanity.com/paper/2005.03501/) (2020)
- [The TUM LapChole dataset for the M2CAI 2016 workflow challenge](https://mlsanity.com/paper/1610.09278/) (2016)
- [The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods](https://mlsanity.com/paper/2104.03178/) (2021)
- [Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery](https://mlsanity.com/paper/2006.16166/) (2020)
- [MIcro-Surgical Anastomose Workflow recognition challenge report](https://mlsanity.com/paper/2103.13111/) (2021)
- [Trajectory-aligned Space-time Tokens for Few-shot Action Recognition](https://mlsanity.com/paper/2407.18249/) (2024)
- [Instrument-tissue Interaction Detection Framework for Surgical Video Understanding](https://mlsanity.com/paper/2404.00322/) (2024)
- [Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition](https://mlsanity.com/paper/2508.03695/) (2025)
- [SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos](https://mlsanity.com/paper/2602.05638/) (2026)
- [TrAction: Action Recognition with Sparse Trajectories](https://mlsanity.com/paper/2606.03490/) (2026)

## Abstract (arXiv)

Surgical performance assessment in minimally invasive surgery largely relies on manual expert review, making it time-consuming, subjective, and difficult to scale. While existing surgical video understanding methods address tasks such as instrument segmentation, surgical phase recognition, and action recognition, they do not explicitly capture fine-grained tissue handling, a key indicator of surgical quality. To address this gap, we introduce tissue tension recognition, a new clinically motivated video understanding task for laparoscopic and robot-assisted rectal cancer surgery. To support this task, we construct SurgTension, the first expert-annotated tissue tension dataset, providing a benchmark for objective tissue tension recognition. We further propose TensionTRAC, a lightweight trajectory-based framework that models tissue tension from sparse point trajectories. Using a compact trajectory encoder, TensionTRAC achieves competitive performance against strong pretrained video backbones.