Writing study: Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion

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← CVPR 2026 oral writing corpus

Zengyi Yang, Yu Liu, Juan Cheng, Zhiqin Zhu, Yafei Zhang, Huafeng Li · 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: Architecture & representation (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

≈ 8.13 page-equivalents

Main figures / tables

8 / 5

Attached appendix figures / tables

0 / 0

Attached appendix span

Separate file recorded below; not in this PDF total

Before references / appendix: ≈ 73.91%References / intervening material: ≈ 26.09%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	1	≈ 1.16 pp.	10.55%
2. Related Work	2	≈ 0.72 pp.	6.55%
3. Methodology	3	≈ 2.55 pp.	23.18%
4. Experiments	5	≈ 3.02 pp.	27.45%
5. Conclusion	8	≈ 0.22 pp.	2%
Acknowledgments	9	≈ 0.08 pp.	0.73%
References	9	≈ 2.87 pp.	26.09%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 3
• Figure 3 · p. 5
• Table 1 · p. 6
• Table 2 · p. 6
• Figure 4 · p. 7
• Table 3 · p. 7
• Figure 5 · p. 7
• Figure 6 · p. 8
• Figure 7 · p. 8
• Table 4 · p. 8
• Table 5 · p. 8
• Figure 8 · 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 5 physical pages; 4 detected figures and 8 tables. These may include overlapping main-text material.

Located appendix only: starts on physical p.1; ≈ 4.93 page-equivalents, 4 figure labels and 8 table labels. This interval is distinct from the whole file.

Detected heading	PDF page	Estimated span
Supplementary Material	1	≈ 0.02 pp.
1. More Details of VFN	1	≈ 0.33 pp.
2. More Comparisons on Fusion Performance	1	≈ 0.39 pp.
3. More Comparisons on an Advanced Detec-	1	≈ 0.1 pp.
4. More Ablation Studies	1	≈ 1.92 pp.
5. Hyperparameter Analysis	3	≈ 0.93 pp.
6. Convergence Analysis	4	≈ 0.06 pp.
7. More Complexity Analysis	4	≈ 0.12 pp.
8. Limitations and Future Work	4	≈ 0.24 pp.
References	5	≈ 0.83 pp.

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

Argument and organization

1. Problem and contribution

   Frames the gap as adapting one fusion system to multiple downstream semantic requirements rather than merely improving a visual fusion score.

2. Argument sequence

   Splits vision-guided fusion, closed-loop optimization and semantic compensation into separately named method subsections.

3. Evidence and verification

   Separates fusion metrics from downstream detection/segmentation/saliency metrics and includes trainable parameters and FLOPs with the task comparison.

Problem, claim and contribution

Frames the gap as adapting one fusion system to multiple downstream semantic requirements rather than merely improving a visual fusion score.

Introduction and Figure 1, p. 1 · Main paper · Additional source 2

Argument and section order

Splits vision-guided fusion, closed-loop optimization and semantic compensation into separately named method subsections.

Sections 3.2–3.4, pp. 3–5 · Main paper · Additional source 2

Figures, captions and visual explanation

Figure 2 distinguishes pre/post-compensation streams, feedback, basis-vector inputs and frozen/updated stages with a full legend, connecting architecture to optimization.

Figure 2, p. 3 · Main paper · Additional source 2

Tables, comparisons and evidence

Separates fusion metrics from downstream detection/segmentation/saliency metrics and includes trainable parameters and FLOPs with the task comparison.

Tables 1–2, p. 6 · Main paper · Additional source 2

Appendix and reproducibility

The supplement adds convergence and component cost accounting, with explicit limitations and complex-scene results.

Supplement Sections 6–8, pp. 4–5 · Main paper · Additional source 2

Clarity and writing conventions

Results are organized by comparison regime—task-network retraining, joint training and cross-detector generalization—not just by dataset.

Sections 4.3–4.4, pp. 6–7 · Main paper · Additional source 2

What to reuse—and what not to infer

Transferable lesson: Map each claimed benefit to its own evidence family: image quality, downstream adaptation, cross-model transfer and resource accounting.

Boundary: Table 1 contains metric non-wins and Table 5 has ablations that win some saliency metrics; avoid saying the full model is best on every measure.

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

Writing-oriented scan throughout Sections 1–5, PDF pp. 1–8; references pp. 9–11: abstract/framing, section sequence, figure/table captions, evaluation organization and conclusions; not an exhaustive technical verification.

Appendix / supplement

Writing-oriented supplement scan: Supplement Sections 1–8, pp. 1–5: VFN architecture, advanced detector comparison, ablations, hyperparameters, convergence, complexity and limits. Video/code assets were not executed or 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 CVF open-access CVPR 2026 accepted-paper PDF · 11 pages
• Official CVF supplementary PDF · 5 pages