arxiv: 2601.11301 · v2 · submitted 2026-01-16 · 💻 cs.CV

Recognition: no theorem link

SAMannot: A Memory-Efficient, Local, Open-source Framework for Interactive Video Instance Segmentation based on SAM2

Gergely Dinya , Andr\'as Gelencs\'er , Krisztina Kup\'an , Clemens K\"upper , Krist\'of Karacs , Anna Gelencs\'er-Horv\'ath

Authors on Pith no claims yet

Pith reviewed 2026-05-16 13:34 UTC · model grok-4.3

classification 💻 cs.CV

keywords video instance segmentationSAM2open-source frameworkhuman-in-the-looplocal processingannotation toolmemory efficiencyinstance tracking

0 comments

The pith

SAMannot adapts SAM2 into a local open-source framework for interactive video instance segmentation with persistent identities and reduced resource demands.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SAMannot as a framework that integrates a modified version of SAM2 to enable precise, human-in-the-loop video instance segmentation entirely on local hardware. It adds a processing layer to cut computational overhead, introduces persistent instance identity tracking, and uses barrier frames plus mask skeletonization for automated prompting and refinement. These elements support output in YOLO and PNG formats along with structured logs, making the system suitable for generating research datasets. Tests on animal behavior videos and subsets of LVOS and DAVIS benchmarks show it functions as a private, cost-effective option compared to cloud or commercial alternatives.

Core claim

SAMannot integrates a modified SAM2 dependency with a custom processing layer into a human-in-the-loop workflow that supports persistent instance identity management, an automated lock-and-refine process using barrier frames, and mask-skeletonization-based auto-prompting, allowing efficient creation of annotated video datasets in standard formats while maintaining segmentation performance.

What carries the argument

The modified SAM2 dependency plus added processing layer that minimizes overhead for responsive interaction and supports persistent identity management across video frames.

If this is right

Users can produce YOLO and PNG formatted datasets locally for downstream machine learning tasks.
The lock-and-refine workflow with barrier frames reduces manual effort in tracking objects over long video sequences.
Structured interaction logs enable reproducibility and analysis of the annotation process in research settings.
The local design eliminates data transfer to external services, supporting privacy-sensitive applications like animal behavior studies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The skeletonization prompting technique could be tested for extension to other promptable models in domains such as medical video analysis.
Persistent identity management might reduce error accumulation in semi-automatic annotation pipelines beyond video, such as in multi-object tracking datasets.
Memory optimizations demonstrated here suggest similar dependency modifications could be applied to other large foundation models for edge-device deployment.

Load-bearing premise

The modifications to SAM2 and the added processing layer preserve the original segmentation accuracy without introducing significant errors or quality loss.

What would settle it

Running SAMannot and unmodified SAM2 on the full LVOS and DAVIS datasets and comparing per-frame IoU or J&F scores would show whether accuracy holds after the changes.

Figures

Figures reproduced from arXiv: 2601.11301 by Andr\'as Gelencs\'er, Anna Gelencs\'er-Horv\'ath, Clemens K\"upper, Gergely Dinya, Krist\'of Karacs, Krisztina Kup\'an.

**Figure 2.** Figure 2: The graphical user interface is built up from the control panel (left), the canvas (right), and the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Control flow for annotating a single block: the input video is processed in blocks to enable efficient [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 5.** Figure 5: Illustrative frames from the analyzed DAVIS sequences for the performance metrics. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

**Figure 4.** Figure 4: Qualitative examples of segmentation results on images from the DAVIS 2017 dataset. The columns [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

**Figure 6.** Figure 6: Illustrative frames from the analyzed LVOS sequences, demonstrating the visual diversity of the [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗

**Figure 7.** Figure 7: Comparison of semantic segmentation boundaries. From left to right: original frame of the [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Examples of ground-truth inconsistencies in LVOS (top) and the consistent annotation achieved [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗

**Figure 9.** Figure 9: A system resources monitor, accessible via a pop-up from the main control window, provides real [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

**Figure 10.** Figure 10: Illustration of User guide windows(A). 16 [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗

**Figure 11.** Figure 11: Illustration of User guide windows(B). 17 [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗

**Figure 12.** Figure 12: Qualitative examples of segmentation results on images from the DAVIS 2017 [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗

read the original abstract

Current research workflows for precise video segmentation are often forced into a compromise between labor-intensive manual curation, costly commercial platforms, and/or privacy-compromising cloud-based services. The demand for high-fidelity video instance segmentation in research is often hindered by the bottleneck of manual annotation and the privacy concerns of cloud-based tools. We present SAMannot, an open-source, local framework that integrates the Segment Anything Model 2 (SAM2) into a human-in-the-loop workflow. To address the high resource requirements of foundation models, we modified the SAM2 dependency and implemented a processing layer that minimizes computational overhead and maximizes throughput, ensuring a highly responsive user interface. Key features include persistent instance identity management, an automated ``lock-and-refine'' workflow with barrier frames, and a mask-skeletonization-based auto-prompting mechanism. SAMannot facilitates the generation of research-ready datasets in YOLO and PNG formats alongside structured interaction logs. Verified through animal behavior tracking use-cases and subsets of the LVOS and DAVIS benchmark datasets, the tool provides a scalable, private, and cost-effective alternative to commercial platforms for complex video annotation tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SAMannot is a practical local tool that adds workflow features to SAM2 for video annotation, but it needs head-to-head accuracy numbers to back the claim that the efficiency changes preserve quality.

read the letter

The core of this paper is a working open-source framework that runs SAM2 locally for interactive video instance segmentation. It adds persistent instance tracking, barrier-frame refinement, and mask-skeleton auto-prompting on top of memory optimizations to the base model. These pieces address real workflow issues like keeping identities stable across frames and reducing manual prompting, which matters for people labeling long videos without cloud services or commercial licenses. The animal-tracking use cases and subsets of LVOS and DAVIS show it can handle typical research data, and the output formats (YOLO and PNG plus logs) are straightforward to use downstream. That combination is the actual contribution: a packaged, privacy-friendly pipeline rather than a new algorithm. The modifications to SAM2 for lower overhead are described clearly enough that someone could reimplement the processing layer. The paper does not overclaim scientific novelty; it positions the work as an engineering solution. The soft spot is the missing quantitative check. The central promise is that the changes keep segmentation accurate while making the interface responsive, yet the text gives no direct comparison of J&F or boundary scores between the modified pipeline and the original SAM2 checkpoint on the same frames. The use-case verification is mentioned but not broken down with error analysis or ablations on the auto-prompting step. Without those numbers, it is difficult to judge whether the optimizations introduce drift or if the skeleton prompting compensates. This paper is for labs that annotate video regularly and need a free, local option. It will not shift the research frontier, but it can save time for practitioners who already rely on SAM2. I would send it to peer review so referees can examine the code and request the accuracy benchmarks that would make the claims tighter.

Referee Report

2 major / 2 minor

Summary. The paper presents SAMannot, an open-source local framework for interactive video instance segmentation that integrates a modified version of SAM2. It describes modifications to SAM2 for memory efficiency, an added processing layer for responsive interaction, features including persistent instance identity management, a lock-and-refine workflow with barrier frames, and mask-skeletonization-based auto-prompting. The tool outputs research-ready annotations in YOLO and PNG formats with interaction logs. It claims verification on animal behavior tracking use-cases and subsets of the LVOS and DAVIS benchmarks as a scalable, private, cost-effective alternative to commercial platforms.

Significance. If the modifications to SAM2 preserve segmentation accuracy while enabling local responsive use, SAMannot would offer a practical open-source tool for researchers needing privacy-preserving video annotation without cloud services or commercial costs. The persistent identity and auto-prompting features address real workflow bottlenecks in complex video tasks such as animal tracking.

major comments (2)

[Evaluation] Evaluation section: verification is reported on animal-tracking use-cases and LVOS/DAVIS subsets, but no quantitative metrics (J&F, mIoU, boundary F-score) or head-to-head comparison to the unmodified SAM2 checkpoint on identical frames are provided, leaving the central claim that modifications preserve accuracy without supporting data.
[Methods] Methods section describing SAM2 modifications and the added processing/auto-prompting layer: no ablation studies or error analysis isolate the effect of memory optimizations or the auto-prompting mechanism on segmentation quality, making it impossible to verify the weakest assumption that accuracy is preserved.

minor comments (2)

[Abstract] Abstract: the phrase 'verified through' is used without accompanying metrics or error analysis, which should be clarified to avoid overstatement.
[Implementation] The manuscript would benefit from explicit links to the code repository, installation instructions, and example interaction logs to support reproducibility claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the manuscript to strengthen the evaluation and methods sections with the requested quantitative results and analyses.

read point-by-point responses

Referee: [Evaluation] Evaluation section: verification is reported on animal-tracking use-cases and LVOS/DAVIS subsets, but no quantitative metrics (J&F, mIoU, boundary F-score) or head-to-head comparison to the unmodified SAM2 checkpoint on identical frames are provided, leaving the central claim that modifications preserve accuracy without supporting data.

Authors: We agree that quantitative metrics and direct comparisons are necessary to substantiate the claim that the SAM2 modifications preserve segmentation accuracy. The current manuscript reports verification on animal-tracking use-cases and LVOS/DAVIS subsets but does not include the specific metrics or head-to-head results. In the revised version, we will add a new subsection to the Evaluation section reporting J&F, mIoU, and boundary F-scores on the benchmark subsets, together with comparisons against the unmodified SAM2 checkpoint on identical frames. This addition will directly address the gap in supporting data. revision: yes
Referee: [Methods] Methods section describing SAM2 modifications and the added processing/auto-prompting layer: no ablation studies or error analysis isolate the effect of memory optimizations or the auto-prompting mechanism on segmentation quality, making it impossible to verify the weakest assumption that accuracy is preserved.

Authors: We acknowledge that ablation studies and error analysis are required to isolate the impact of the memory optimizations and auto-prompting on segmentation quality. The Methods section describes these components but does not provide the requested ablations. We will revise the Methods section to include ablation experiments that evaluate segmentation quality with and without each modification on the LVOS and DAVIS subsets, accompanied by error analysis. These additions will allow verification that accuracy is preserved. revision: yes

Circularity Check

0 steps flagged

No circularity: software framework description with no derivations or fitted predictions

full rationale

The paper presents SAMannot as an open-source local framework integrating and modifying SAM2 for interactive video segmentation. It contains no mathematical equations, parameter fitting, predictions, or first-principles derivations. Claims rest on feature descriptions (persistent identity, lock-and-refine workflow, auto-prompting) and verification via use-cases plus benchmark subsets, without any self-referential reduction of outputs to inputs. No self-citations of theorems or ansatzes appear. This is a standard engineering/tool paper whose central contribution is implementation and workflow design, not a derived result.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied software engineering contribution with no free parameters, mathematical axioms, or newly postulated entities; all components derive from the existing SAM2 model and standard computer vision practices.

pith-pipeline@v0.9.0 · 5534 in / 1037 out tokens · 30767 ms · 2026-05-16T13:34:17.914269+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 1 internal anchor

[1]

Ultralytics yolo11, 2024

Glenn Jocher and Jing Qiu. Ultralytics yolo11, 2024

work page 2024
[2]

Murthy, George Lauder, Catherine Dulac, M

Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mo- hammed Mostafizur Rahman, Valentina Di Santo, Daniel Soberanes, Guoping Feng, Venkatesh N. Murthy, George Lauder, Catherine Dulac, M. Mathis, and Alexander Mathis. Multi-animal pose es- timation, identification and tracking with deeplabcut.Nature Methods, 19:496 – 504, 2022

work page 2022
[3]

Dwyer, J

B. Dwyer, J. Nelson, T. Hansen, et al. Roboflow (version 1.0) [software], 2025. Computer vision platform

work page 2025
[4]

Cord Technologies

Inc. Cord Technologies. Encord

work page
[5]

Tensor Matics

Inc. Tensor Matics. Labellerr

work page
[6]

Berg, Wan-Yen Lo, Piotr Doll´ ar, and Ross Girshick

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Doll´ ar, and Ross Girshick. Segment any- thing, 2023

work page 2023
[7]

CVAT.ai Corporation. Cvat

work page
[8]

Kappeler, Claudia Fichtel, and Alexander S

Ozan Kanbertay, Richard Vogg, Elif Karakoc, Peter M. Kappeler, Claudia Fichtel, and Alexander S. Ecker. Silvi: Simple interface for labeling video interactions, 2025. Accessed: 2025-12-21

work page 2025
[9]

The VIA annotation software for images, audio and video

Abhishek Dutta and Andrew Zisserman. The VIA annotation software for images, audio and video. In Proceedings of the 27th ACM International Conference on Multimedia, MM ’19, New York, NY, USA,

work page
[10]

Sam 2: Segment anything in images and videos.arXiv preprint, 2024

Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman R¨ adle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Doll´ ar, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos.arXiv preprint, 2024

work page 2024
[11]

tkinter — Python interface to Tcl/Tk

Python Software Foundation. tkinter — Python interface to Tcl/Tk. Python 3 Documentation. Accessed: 2025-12-20. 19

work page 2025
[12]

Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman. The PASCAL visual object classes (VOC) challenge.International Journal of Computer Vision, 88(2):303– 338, 2010

work page 2010
[13]

Are there any method for reducing gpu memory overhead? (issue #196)

aza1200. Are there any method for reducing gpu memory overhead? (issue #196). GitHub issue in facebookresearch/sam2, August 2024. Accessed: 2025-12-21

work page 2024
[14]

Sam2 for segmenting a 2 hour video? (issue #264)

aendrs. Sam2 for segmenting a 2 hour video? (issue #264). GitHub issue infacebookresearch/sam2, August 2024. Accessed: 2025-12-21

work page 2024
[15]

A benchmark dataset and evaluation methodology for video object segmentation

Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine-Hornung. A benchmark dataset and evaluation methodology for video object segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

work page 2016
[16]

The 2017 DAVIS Challenge on Video Object Segmentation

Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbel´ aez, Alexander Sorkine-Hornung, and Luc Van Gool. The 2017 davis challenge on video object segmentation.arXiv:1704.00675, 2017

work page internal anchor Pith review Pith/arXiv arXiv 2017
[17]

Lvos: A benchmark for large-scale long-term video object segmentation.arXiv preprint arXiv:2404.19326, 2024

Lingyi Hong, Zhongying Liu, Wenchao Chen, Chenzhi Tan, Yuang Feng, Xinyu Zhou, Pinxue Guo, Jinglun Li, Zhaoyu Chen, Shuyong Gao, et al. Lvos: A benchmark for large-scale long-term video object segmentation.arXiv preprint arXiv:2404.19326, 2024

work page arXiv 2024
[18]

Mark Everingham, Luc Van Gool, C. K. I. Williams, J. Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge, 2010

work page 2010
[19]

C. J. Van Rijsbergen.Information Retrieval. Butterworth-Heinemann, 2nd edition, 1979

work page 1979
[20]

Objective criteria for the evaluation of clustering methods.Journal of the American Statistical association, 66(336):846–850, 1971

William M Rand. Objective criteria for the evaluation of clustering methods.Journal of the American Statistical association, 66(336):846–850, 1971

work page 1971
[21]

Version 535.183.01

NVIDIA Corporation.NVIDIA System Management Interface (nvidia-smi), 2024. Version 535.183.01

work page 2024
[22]

Sam 3: Seg- ment anything with concepts, 2025

Nicolas Carion, Laura Gustafson, Yuan-Ting Hu, Shoubhik Debnath, Ronghang Hu, Didac Suris, Chai- tanya Ryali, Kalyan Vasudev Alwala, Haitham Khedr, Andrew Huang, Jie Lei, Tengyu Ma, Baishan Guo, Arpit Kalla, Markus Marks, Joseph Greer, Meng Wang, Peize Sun, Roman R¨ adle, Triantafyllos Afouras, Effrosyni Mavroudi, Katherine Xu, Tsung-Han Wu, Yu Zhou, Lili...

work page 2025