# TSMS-SAM2: Multi-scale Temporal Sampling Augmentation and Memory-Splitting Pruning for Promptable Video Object Segmentation and Tracking in Surgical Scenarios

arXiv: https://arxiv.org/abs/2508.05829 · HTML: https://mlsanity.com/paper/2508.05829/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 48 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 30 of 31)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [Video Instance Segmentation](https://mlsanity.com/paper/1905.04804/) (2019)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [Temporal Relational Reasoning in Videos](https://mlsanity.com/paper/1711.08496/) (2017)
- [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://mlsanity.com/paper/2306.00989/) (2023)
- [Personalize Segment Anything Model with One Shot](https://mlsanity.com/paper/2305.03048/) (2023)
- [Track Anything: Segment Anything Meets Videos](https://mlsanity.com/paper/2304.11968/) (2023)
- [Associating Objects with Transformers for Video Object Segmentation](https://mlsanity.com/paper/2106.02638/) (2021)
- [SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation](https://mlsanity.com/paper/2308.08746/) (2023)
- [TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation](https://mlsanity.com/paper/1801.05746/) (2018)
- [ISINet: An Instance-Based Approach for Surgical Instrument Segmentation](https://mlsanity.com/paper/2007.05533/) (2020)
- [Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video](https://mlsanity.com/paper/1907.07899/) (2019)
- [VideoMix: Rethinking Data Augmentation for Video Classification](https://mlsanity.com/paper/2012.03457/) (2020)
- [Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction](https://mlsanity.com/paper/2002.09905/) (2020)
- [Video Object Segmentation and Tracking: A Survey](https://mlsanity.com/paper/1904.09172/) (2019)
- [Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning](https://mlsanity.com/paper/2408.07931/) (2024)
- [MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation](https://mlsanity.com/paper/2303.09514/) (2023)
- [From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments](https://mlsanity.com/paper/2211.16200/) (2022)
- [MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networks](https://mlsanity.com/paper/2203.09303/) (2022)
- [SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost](https://mlsanity.com/paper/2506.01304/) (2025)
- [Feature Re-Learning with Data Augmentation for Video Relevance Prediction](https://mlsanity.com/paper/2004.03815/) (2020)

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

- [Lean-SAM2: Target-Anchored Memory and Encoder Acceleration for SAM2](https://mlsanity.com/paper/2607.19811/) (2026)

## Abstract (arXiv)

Promptable video object segmentation and tracking (VOST) has seen significant advances with the emergence of foundation models like Segment Anything Model 2 (SAM2); however, their application in surgical video analysis remains challenging due to complex motion dynamics and the redundancy of memory that impedes effective learning. In this work, we propose TSMS-SAM2, a novel framework that enhances promptable VOST in surgical videos by addressing challenges of rapid object motion and memory redundancy in SAM2. TSMS-SAM2 introduces two key strategies: multi-temporal-scale video sampling augmentation to improve robustness against motion variability, and a memory splitting and pruning mechanism that organizes and filters past frame features for more efficient and accurate segmentation. Evaluated on EndoVis2017 and EndoVis2018 datasets, TSMS-SAM2 achieved the highest mean Dice scores of 95.24 and 86.73, respectively, outperforming prior SAM-based and task-specific methods. Extensive ablation studies confirm the effectiveness of multiscale temporal augmentation and memory splitting, highlighting the framework's potential for robust, efficient segmentation in complex surgical scenarios. Our source code will be available at https://github.com/apple1986/TSMS-SAM2.