Writing study: RobotSeg: A Model and Dataset for Segmenting Robots in Image and Video

CVPR overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← CVPR 2026 oral writing corpus

Haiyang Mei, Qiming Huang, Hai Ci, Mike Zheng Shou · Official CVPR 2026 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Datasets, benchmarks & measurement (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF

11 physical pages

Before references / appendix

≈ 8.05 page-equivalents

Main figures / tables

9 / 4

Attached appendix figures / tables

0 / 0

Attached appendix span

Separate file recorded below; not in this PDF total

Before references / appendix: ≈ 73.18%References / intervening material: ≈ 26.82%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Extraction notes: No attached appendix boundary detected. Zero means no recognized caption, not independently verified absence.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level heading	Starts on PDF page	Estimated span	Share of entire PDF
1. Introduction	2	≈ 0.69 pp.	6.27%
2. We construct VRS, the first video robot segmenta-	2	≈ 0.04 pp.	0.36%
3. We design a novel framework that integrates structure-	2	≈ 1.63 pp.	14.82%
4. Video Robot Segmentation Model	4	≈ 2.44 pp.	22.18%
5. Experiments	6	≈ 1.55 pp.	14.09%
6. Conclusion	8	≈ 0.65 pp.	5.91%
References	9	≈ 2.95 pp.	26.82%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 3
• Figure 3 · p. 4
• Table 1 · p. 4
• Figure 4 · p. 5
• Figure 5 · p. 5
• Figure 6 · p. 6
• Figure 7 · p. 7
• Figure 8 · p. 7
• Figure 9 · p. 8
• Table 2 · p. 8
• Table 3 · p. 8
• Table 4 · p. 8

Attached appendix

No recognized caption labels.

Separate source files—excluded from canonical-PDF totals

A separate author version may repeat main text and references. Whole-file lengths and counts are not extra appendix material and are never added to the canonical PDF totals. Read the study's source scope for which sections received substantive reading versus layout inspection.

Additional source 1

Separate PDF (or ZIP containing it): whole-file layout of 14 physical pages; 11 detected figures and 4 tables. These may include overlapping main-text material.

Detected heading	PDF page	Estimated span
1. Overview	1	≈ 0.15 pp.
2. An Application Example of RobotSeg	1	≈ 0.44 pp.
3. More VRS Dataset Examples	1	≈ 0.17 pp.
4. Category-Wise Analysis Across Diverse	1	≈ 3.93 pp.
5. More Visual Comparison Results	5	≈ 0.71 pp.
6. Computational Efficiency Analysis	6	≈ 5.56 pp.
7. Comparison with SAM 3	12	≈ 0.19 pp.
8. Architecture Details	12	≈ 0.59 pp.
9. Limitation and Future Work	12	≈ 0.87 pp.
References	13	≈ 1.22 pp.

• Figure 1 · p. 2
• Figure 2 · p. 3
• Figure 3 · p. 4
• Table 1 · p. 5
• Table 2 · p. 5
• Table 3 · p. 6
• Table 4 · p. 6
• Figure 4 · p. 7
• Figure 5 · p. 8
• Figure 6 · p. 9
• Figure 7 · p. 10
• Figure 8 · p. 11
• Figure 9 · p. 13
• Figure 10 · p. 13
• Figure 11 · p. 13

Argument and organization

1. Dataset before architecture

   Define robot-specific visual challenges and show sequences/annotations before model internals.

2. Prompts, training and evaluation settings

   Explain memory, automatic prompt generation and first-frame supervision before comparing interaction modes.

Problem, claim and contribution

The opening ties robot segmentation to articulated structures, prompt dependence and annotation cost, then maps those three limitations to three model/training interventions.

Abstract and Sections 1 and 4, PDF pp. 1–4 · Main paper · Additional source 2

Argument and section order

A dedicated dataset section precedes the model, preventing the corpus contribution from becoming merely a line in experimental setup.

Sections 3–4, PDF pp. 3–4 · Main paper · Additional source 2

Figures, captions and visual explanation

The dataset montage aligns RGB sequences with arm/gripper masks. On page 6, the prompt-generator schematic is paired with explicit clustering pseudocode, connecting the visual concept to implementable operations.

Figure 2, PDF p. 3; Figure 6 and Algorithm 1, PDF p. 6; visual inspection · Main paper · Additional source 2

Tables, comparisons and evidence

Comparison tables split automatic, click, box and interactive modes and use a dash for unsupported modes, making the amount of user input part of the comparison contract.

Tables 2–3, PDF p. 8 · Main paper · Additional source 2

Appendix and reproducibility

The supplement adds embodiment-wise scores, efficiency, architecture details and a limitation section, distinguishing average performance from category-specific strengths and weaknesses.

Supplement Sections 4, 6 and 8–9, PDF pp. 5–6 and 12–13 · Main paper · Additional source 2

Clarity and writing conventions

Loss subsections explain the supervision level—video, object and patch—rather than listing unexplained consistency terms.

Section 4.3 and Figure 7, PDF pp. 6–7 · Main paper · Additional source 2

What to reuse—and what not to infer

Transferable lesson: For a model-plus-dataset contribution, show annotation semantics early and stratify all results by interaction budget and target granularity.

Boundary: Automatic and manually initialized segmentation are different settings; model size, FLOPs and actual per-frame latency also should not be treated as interchangeable efficiency measures.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-09-26

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Manuscript-wide writing scan of PDF pp. 1–11: framing, argument sequence, section organization, figure/table captions, evaluation interpretation and limitations; reference sections identified. This is a writing-focused scan, not an exhaustive technical proof or reproduction audit. Main PDF pages 3 and 6 were rendered and visually inspected.

Appendix / supplement

Writing-focused scan of the separate supplementary PDF, pp. 1–14, including its organization, captions, protocol/evidence detail and limitation material where present; proofs and code listings were inspected for exposition, not independently verified or executed. Supplement Sections 4, 6 and 8–9, PDF pp. 5–6 and 12–13. Linked video/audio demonstrations and non-PDF archive contents were not assessed.

Visually inspected PDF pages

3, 6

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.

• Official CVPR 2026 CVF open-access accepted manuscript; retrieved 2026-09-26 · 11 pages
• Official CVPR 2026 CVF supplementary PDF; retrieved 2026-09-26 · 14 pages