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Writing study: CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal

← ICML 2026 oral writing corpus

Qingdong He, Chaoyi Wang, Peng Tang, Yifan Yang, Xiaobin Hu · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Domain-grounded methods & physical systems (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
14 physical pages
Before references / appendix
≈ 9.11 page-equivalents
Main figures / tables
6 / 3
Attached appendix figures / tables
4 / 0
Attached appendix span
≈ 2.91 page-equivalents
Before references / appendix: ≈ 65.07%References / intervening material: ≈ 14.14%Attached appendix: ≈ 20.79%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: The physical p.12 PNG shows two body-size bold appendix headings omitted by the size filter: A at y=89.521806/792 and B at y=378.069806/792. Retain the existing Appendix start, and restore A/B in the single-column page coordinates. Newly detected unnumbered impact/singular-acknowledgment statements are retained from the size-aware source extraction; authored heading corrections are unchanged.

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 headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction2≈ 1.14 pp.8.14%
2. Related Work3≈ 0.52 pp.3.71%
3. Method3≈ 2.86 pp.20.43%
4. Experiments6≈ 3.24 pp.23.14%
5. Conclusion9≈ 0.2 pp.1.43%
Impact Statement10≈ 0.07 pp.0.5%
References10≈ 1.98 pp.14.14%
Appendix12≈ 0.03 pp.0.21%
A. Two-Stage Training Pipeline12≈ 0.36 pp.2.57%
B. More Qualitative Results12≈ 2.52 pp.18%
All detected figure and table locations

Argument and organization

  1. Problem and contribution

    Enumerates three limitations, three required capabilities and three corresponding innovations, giving the application constraints a traceable method mapping.

  2. Argument sequence

    Prior learning and adaptive weighting are separated from deployment, with an explicit inference subsection before evaluation.

  3. Evidence and verification

    The main numerical table states rank, sampling steps and configuration; it preserves slower elapsed inference than two baselines despite stronger quality metrics.

Problem, claim and contribution

Enumerates three limitations, three required capabilities and three corresponding innovations, giving the application constraints a traceable method mapping.

Introduction, p. 2 · Main paper

Argument and section order

Prior learning and adaptive weighting are separated from deployment, with an explicit inference subsection before evaluation.

Sections 3.2-3.4, pp. 3-6 · Main paper

Figures, captions and visual explanation

The overview has distinct training and inference lanes; the results page groups metrics by reconstruction, perception, temporal behavior, flow and time beside matched qualitative crops.

Figure 2, p. 2; Table 1 and Figure 5, p. 8; both PNGs inspected · Main paper

Tables, comparisons and evidence

The main numerical table states rank, sampling steps and configuration; it preserves slower elapsed inference than two baselines despite stronger quality metrics.

Table 1, p. 8 · Main paper

Appendix and reproducibility

The appendix repeats the two-stage contract and captions the displayed mask overlays as training-time guidance, not required inference inputs.

Appendix A, p. 12; Figures A3-A4, p. 14 · Main paper

Clarity and writing conventions

Named limitations/capabilities make the introduction navigable, but claims of complete residue-free removal and language-agnostic behavior outrun the selected qualitative examples.

Introduction, p. 2; qualitative/cross-lingual discussion, p. 8 · Main paper

What to reuse—and what not to infer

Transferable lesson: Separate training supervision, internal guidance and deployment inputs in both prose and the pipeline diagram.

Boundary: No manual mask annotation does not mean unpaired or supervision-free training; static example frames alone cannot verify temporal consistency or universal cross-lingual removal.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked
2026-10-07
Reading status
Writing-focused manuscript scan + selected visuals inspected
Main text
Writing-focused distributed manuscript scan, not exhaustive technical/proof validation. Sections 1-5, pp. 1-9: subtitle-specific constraints, two-stage training, mask-free inference, comparisons and ablations.
Appendix / supplement
Writing-focused appendix scan; code, data and external assets not executed. Appendices A-B, pp. 12-14: training/inference equations and extra qualitative/cross-lingual examples.
Visually inspected PDF pages
2, 8

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.