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
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 heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 2 | ≈ 1.14 pp. | 8.14% |
| 2. Related Work | 3 | ≈ 0.52 pp. | 3.71% |
| 3. Method | 3 | ≈ 2.86 pp. | 20.43% |
| 4. Experiments | 6 | ≈ 3.24 pp. | 23.14% |
| 5. Conclusion | 9 | ≈ 0.2 pp. | 1.43% |
| Impact Statement | 10 | ≈ 0.07 pp. | 0.5% |
| References | 10 | ≈ 1.98 pp. | 14.14% |
| Appendix | 12 | ≈ 0.03 pp. | 0.21% |
| A. Two-Stage Training Pipeline | 12 | ≈ 0.36 pp. | 2.57% |
| B. More Qualitative Results | 12 | ≈ 2.52 pp. | 18% |
All detected figure and table locations
Main text
Attached appendix
Argument and organization
- Problem and contribution
Enumerates three limitations, three required capabilities and three corresponding innovations, giving the application constraints a traceable method mapping.
- Argument sequence
Prior learning and adaptive weighting are separated from deployment, with an explicit inference subsection before evaluation.
- 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.