# Where It Moves, It Matters: Referring Surgical Instrument Segmentation via Motion

arXiv: https://arxiv.org/abs/2601.12224 · HTML: https://mlsanity.com/paper/2601.12224/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 25 of 25)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [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)
- [MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions](https://mlsanity.com/paper/2308.08544/) (2023)
- [Associating Objects with Transformers for Video Object Segmentation](https://mlsanity.com/paper/2106.02638/) (2021)
- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80](https://mlsanity.com/paper/2012.12453/) (2020)
- [Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer](https://mlsanity.com/paper/2206.11053/) (2022)
- [Pixel-Wise Recognition for Holistic Surgical Scene Understanding](https://mlsanity.com/paper/2401.11174/) (2024)
- [Advancing Surgical VQA with Scene Graph Knowledge](https://mlsanity.com/paper/2312.10251/) (2023)
- [MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation](https://mlsanity.com/paper/2303.09514/) (2023)
- [A spatio-temporal network for video semantic segmentation in surgical videos](https://mlsanity.com/paper/2306.11052/) (2023)
- [CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools](https://mlsanity.com/paper/2312.07352/) (2023)
- [Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments](https://mlsanity.com/paper/1910.11109/) (2019)
- [ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking](https://mlsanity.com/paper/2505.08581/) (2025)
- [CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis](https://mlsanity.com/paper/2310.04414/) (2023)
- [Highway Driving Dataset for Semantic Video Segmentation](https://mlsanity.com/paper/2011.00674/) (2020)
- [SegMatch: A semi-supervised learning method for surgical instrument segmentation](https://mlsanity.com/paper/2308.05232/) (2023)
- [Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video](https://mlsanity.com/paper/2407.15794/) (2024)
- [Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic](https://mlsanity.com/paper/2310.04416/) (2023)
- [Recognizing Surgical Phases Anywhere: Few-Shot Test-time Adaptation and Task-graph Guided Refinement](https://mlsanity.com/paper/2506.20254/) (2025)
- [Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment](https://mlsanity.com/paper/2411.10919/) (2024)

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

- [UniSurgSAM: A Unified Promptable Model for Reliable Surgical Video Segmentation](https://mlsanity.com/paper/2604.03645/) (2026)
- [ReGround-Surg: Reliability-Guided Anchor Grounding for Referring Surgical Video Segmentation](https://mlsanity.com/paper/2608.24671/) (2026)
- [LD-RSVIS: A Large-Scale and Diverse Benchmark for Referring Surgical Video Instrument Segmentation](https://mlsanity.com/paper/2609.23067/) (2026)

## Abstract (arXiv)

Enabling intuitive, language-driven interaction with surgical scenes is a critical step toward intelligent operating rooms and autonomous surgical robotic assistance. However, the task of referring segmentation, localizing surgical instruments based on natural language descriptions, remains underexplored in surgical videos, with existing approaches struggling to generalize due to reliance on static visual cues and predefined instrument names. In this work, we introduce SurgRef, a novel motion-guided framework that grounds free-form language expressions in instrument motion, capturing how tools move and interact across time, rather than what they look like. This allows models to understand and segment instruments even under occlusion, ambiguity, or unfamiliar terminology. To train and evaluate SurgRef, we present Ref-IMotion, a diverse, multi-institutional video dataset with dense spatiotemporal masks and rich motion-centric expressions. SurgRef achieves state-of-the-art accuracy and generalization across surgical procedures, setting a new benchmark for robust, language-driven surgical video segmentation.