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REVIEW 4 major objections 6 minor 12 references

Track Anything Annotate: Video annotation and dataset generation of computer vision models

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The Track Anything Annotate prototype pairs SAM2 with XMem++ to convert video into YOLO-format training data and claims a significant cut in manual annotation labor.

desk verdict A thin tool paper that claims significant annotation acceleration but never measures it; useful as a code pointer, not as a research contribution. read the letter →

arxiv 2505.17884 v1 pith:B7VW3CFO submitted 2025-05-23 cs.CV

Nikita Ivanov , Mark Klimov , Dmitry Glukhikh , Tatiana Chernysheva , Igor Glukhikh This is my paper
classification cs.CV
keywords videoannotationSAM2XMem++datasetgenerationYOLOformatobjectsegmentationinteractivecomputervision
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper is trying to establish that a two-model pipeline can replace most manual labeling work in computer-vision dataset creation. The tool uses SAM2 to segment an object on the first frame and XMem++ to track the resulting mask through the rest of the video, then exports the masks as YOLO-format labels. The abstract asserts that this prototype significantly accelerates dataset generation compared to manual annotation. If that is right, large video datasets for training vision models could be produced with far less human labor.

What carries the argument

The load-bearing object is the two-stage SAM2+XMem++ pipeline. SAM2 is a promptable segmentation model that accepts points, boxes, or both to produce an object mask in the first frame; XMem++ is a video object segmentation model with a persistent memory module that takes that mask and propagates it frame by frame. The exported masks are written as YOLO annotations, meaning a class file plus per-frame image/text pairs. The paper's Table 1 quantifies the trade-off: SAM2 initializes in 2722 ms and uses 1476 MB of VRAM versus FastSAM's 1357 ms and 607 MB, but produces smoother, less noisy masks.

What would settle it

Take a video with known ground-truth object masks, run the SAM2+XMem++ pipeline, and compute the mean intersection-over-union between the generated masks and the ground truth. If the overlap is low enough that an object detector trained on the generated labels clearly underperforms one trained on human labels, the acceleration claim would not survive.

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Extended reading notes

Core claim

The paper's central claim is that combining SAM2 with XMem++ yields a working prototype for video annotation and dataset generation that significantly accelerates dataset generation compared to manual annotation, while also improving accuracy in complex scenarios such as overlapping objects or changing lighting. The practical result is an architecture choice: SAM2's interactive first-frame segmentation plus XMem++'s persistent-memory tracking is the most effective of the three tested combinations, at the price of higher hardware requirements. The output is a YOLO-formatted dataset containing per-frame images and text files with object coordinates.

Load-bearing premise

The speed advantage assumes the automatically produced masks are accurate enough to be used directly as training labels, because the paper writes them into a dataset format without checking them against human annotation or ground truth.

Editorial extensions

If this is right

  • Objects in a video can be segmented once in the first frame and their masks propagated automatically through all subsequent frames.
  • The resulting per-frame masks can be exported directly as YOLO-format labels, so the output can feed into a training pipeline immediately.
  • For projects needing long-term tracking of small objects, the paper recommends the SAM2+XMem++ combination as the most effective of the three tested.
  • If the central speed claim holds, video datasets that currently require days of manual clicking could be produced in much less working time, reducing the labeling bottleneck.
  • The higher hardware cost of SAM2 is accepted in exchange for smoother contours and less noise than the FastSAM-based alternative.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An untested extension is to train an object detector on the auto-generated YOLO dataset and compare its accuracy against a detector trained on manually annotated data from the same videos; that would measure whether the masks are good enough to serve as training labels.
  • A direct benchmark timing the pipeline against manual labeling on identical videos would turn the qualitative acceleration claim into a number, which the paper does not report.
  • Because XMem++ relies on persistent memory, tracking quality could degrade over very long videos when object appearance changes; inserting an occasional manual re-segmentation frame would test this and may improve reliability.
  • The speed gain should scale with object count and video length, since traditional tools require new annotations per object per frame while this pipeline only needs prompts on the first frame.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper presents a prototype video annotation and dataset-generation tool called “Track Anything Annotate.” The proposed pipeline uses SAM2 to obtain an initial mask from user prompts, then XMem++ to propagate the mask across subsequent video frames, and finally exports the resulting masks as YOLO-format annotations. The authors also describe two earlier prototype variants (OpenCV+FastSAM and FastSAM+XMem) and report a single hardware timing comparison between FastSAM and SAM2 on an RTX 2060 Super. The abstract and conclusion claim that the prototype “significantly accelerates dataset generation compared to manual annotation” and that it “significantly reduces manual intervention,” making it scalable for large-scale vision tasks.

Significance. If the performance and quality claims were supported, the tool would be a useful practical contribution to the video-annotation and dataset-creation ecosystem. The combination of SAM2 for initial segmentation and XMem++ for tracking is a reasonable engineering choice, and the release of the code at the given GitHub repository is a concrete asset that could help practitioners assess and reuse the system. However, the paper is currently a system description with qualitative demonstrations only: there is no empirical evaluation of annotation speed, no comparison to manual annotation, no measurement of mask accuracy against ground truth, and no downstream validation of the generated labels. The claimed significance is therefore unsubstantiated as written, and the practical value remains a suggestion rather than a demonstrated result.

major comments (4)
  1. [Abstract and Conclusion] The central claim that the prototype “significantly accelerates dataset generation compared to manual annotation” is never measured. No end-to-end annotation-time comparison, no manual-annotation baseline, and no throughput metric appear anywhere in the manuscript. Table 1 reports component latencies for model initialization and mask prediction only, which do not cover the full annotation workflow including user interaction, tracking, and correction. A controlled experiment comparing annotation time on the same video set, with and without the tool and with manual correction time included, is required to support this claim.
  2. [Sections 2.3, 2.4, and 4] The claimed segmentation-quality improvement of SAM2 over FastSAM is supported only by visual examples (Figure 4) and by timing/VRAM numbers (Table 1), which contain no accuracy metric. The statement in Section 2.4 that SAM2 “outperforms it in terms of segmentation accuracy” lacks quantitative evidence. Please report mask-quality metrics (e.g., mIoU or boundary accuracy) against annotated ground truth on a standard video-object-segmentation benchmark, and also report the accuracy of the YOLO labels exported by the tool.
  3. [Section 4] The dataset-generation pipeline writes SAM2/XMem++ masks directly into YOLO-format labels with no validation step or accuracy threshold. If the masks drift or fail on challenging frames, the accelerated labels will silently degrade downstream training. Either include a human-in-the-loop verification/editing stage and measure its time cost, or evaluate the exported labels by training a detector on them and comparing against a detector trained on manually labeled data.
  4. [Section 5] The conclusion states that SAM2+XMem++ “has proven to be the most effective” and “significantly reduces manual intervention,” but the paper does not compare the three pipeline variants in terms of tracking quality, failure cases, or annotation speed. The choice of XMem++ over XMem is not quantitatively justified. If this comparison is not provided, the recommendation should be softened to a design rationale rather than a proven claim.
minor comments (6)
  1. [Section 1] The text uses “manual partitioning” where “manual annotation” is intended; please correct the wording throughout.
  2. [Section 1] The phrase “The prototype tool presented is unparalleled” is an unsupported promotional statement; replace it with a concrete description of what is new relative to prior annotation tools.
  3. [Sections 2.3 and 3] The terms “hints” and “cues” are used interchangeably; please unify the terminology.
  4. [Table 1] The table reports “Image Initialization (ms)” and “Mask prediction (ms)” for FastSAM and SAM2 but does not specify whether these are per image or per frame, nor the image resolution used; please clarify the protocol.
  5. [References] Reference [5] misspells “Zisserman” as “Zissermann” and lacks full publication details; please correct and complete the citation.
  6. [General] The GitHub repository is referenced in the abstract but no license or usage instructions are given in the paper; please add a license statement and system requirements for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the pipeline composes externally trained models and no prediction is derived from fitted inputs; the acceleration claim is unsupported but not circular.

full rationale

The paper contains no derivation chain, no equations, and no fitted parameters. The prototype simply composes externally trained models (FastSAM, SAM2, XMem, XMem++) whose weights and capabilities come from prior external publications. The central claim that the tool 'significantly accelerates dataset generation compared to manual annotation' is asserted without a measured manual-annotation baseline, an annotator study, or a throughput comparison; likewise, generated masks are exported as YOLO labels without ground-truth validation. However, an unsupported empirical assertion is a correctness/evidence gap, not circular reasoning. The only self-citation is reference [1], a prior paper from the same group, cited to support the generic statement that manual partitioning can take too long; that claim does not function as a load-bearing premise in any derivation, and the cited work is not used to define or force any result here. No step reduces to its own input by construction, and no fitted parameter is relabeled as a prediction. Therefore the circularity score is 0, while noting that the headline acceleration and label-quality claims remain empirically unverified.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no fitted parameters and no new theoretical entities. It rests entirely on the correctness of the externally trained SAM2 and XMem++ models, and on the unstated assumption that their outputs are reliable enough to serve as training labels. These assumptions are neither verified nor quantified in the text.

assumptions (3)
  • domain assumption SAM2 provides accurate segmentation masks.
    Used as the initial mask generator in the pipeline without any validation of mask quality against ground truth.
  • domain assumption XMem++ propagates masks correctly across frames.
    Assumed to produce accurate tracking, but no quantitative tracking metrics are reported.
  • ad hoc to paper Generated masks are valid ground-truth annotations for downstream training.
    The dataset generation step treats model outputs as labels, but no human verification or accuracy threshold is enforced.

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Cite this review

Pith. "Pith review of Track Anything Annotate: Video annotation and dataset generation of computer vision models." pith.science (2026). https://pith.science/paper/B7VW3CFO

@misc{pith2026250517884,
  author       = {Pith},
  title        = {Pith review of: Track Anything Annotate: Video annotation and dataset generation of computer vision models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B7VW3CFO}},
  note         = {Machine review of arXiv:2505.17884}
}
read the original abstract

Modern machine learning methods require significant amounts of labelled data, making the preparation process time-consuming and resource-intensive. In this paper, we propose to consider the process of prototyping a tool for annotating and generating training datasets based on video tracking and segmentation. We examine different approaches to solving this problem, from technology selection through to final implementation. The developed prototype significantly accelerates dataset generation compared to manual annotation. All resources are available at https://github.com/lnikioffic/track-anything-annotate

Figures

Figures reproduced from arXiv: 2505.17884 by the authors.

Figure 1
Figure 1. Object selection [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Object segmentation. 2.2 FastSAM + XMem This step was aimed at improving object tracking by replacing OpenCV with XMem. This step allowed for more accurate and flexible object tracking in the video. FastSAM [12] was used to segment the first frame of the video. The resulting mask was then passed to XMem [4], which performed object tracking and generated masks on subsequent frames. Image segmentation can be performed… view at source ↗
Figure 3
Figure 3. Segmentation of the image with the resulting mask and the image with the [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Segmentation of the image with the received and applied mask when using [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Masks obtained through FastSAM and SAM2. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Set of frames with superimposed masks obtained after XMem++ work. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Tool prototype interface. of objects are recorded. An example of the generated dataset is shown in [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Formed dataset. Web interface demo. Here is a lightweight version of the application. It allows you to segment the first frame, view the mask, and run object tracking on the video. This creates a set of 100 frames with selected objects. A demo version of the applicatio…
Figure 9
Figure 9. Figure 9: An example of automatic markup of a generated dataset. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Demo interface on HuggingFace [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]
Figure 11
Figure 11. Figure 11: Demo interface on HuggingFace. 7 [PITH_FULL_IMAGE:figures/full_fig_p007_11.png]

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Reference graph

Works this paper leans on

12 extracted references · 10 canonical work pages

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