Writing study: Learning Streaming Video Representation via Multitask Training

ICCV overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← ICCV 2025 oral writing corpus

Yibin Yan, Jilan Xu, Shangzhe Di, Yikun Liu, Yudi Shi, Qirui Chen, Zeqian Li, Yifei Huang, Weidi Xie · Official ICCV 2025 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: Training, objectives & optimization (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

13 physical pages

Before references / appendix

≈ 7.86 page-equivalents

Main figures / tables

3 / 6

Attached appendix figures / tables

0 / 0

Attached appendix span

No boundary detected; not assumed zero

Before references / appendix: ≈ 60.46%References / intervening material: ≈ 39.54%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	≈ 0.88 pp.	6.77%
2. Related Work	2	≈ 0.6 pp.	4.62%
3. Method	3	≈ 2.96 pp.	22.77%
4. Experiments	6	≈ 2.45 pp.	18.85%
5. Conclusions	8	≈ 0.12 pp.	0.92%
Acknowledgement	8	≈ 0.04 pp.	0.31%
References	8	≈ 5.14 pp.	39.54%

All detected figure and table locations

Main text

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

Attached appendix

No recognized caption labels.

Argument and organization

1. 1 Introduction; 2 Related Work

   Defines streaming through causal access, historical context and latency rather than simply short video clips.

2. 3 Method

   Formulates global/frame/patch features, causal temporal encoding, text labels and six multitask losses.

3. 4 Experiments; 5 Conclusions

   Describes annotated pretraining sources and frozen-backbone transfer before task, attention and data-efficiency controls.

Problem, claim and contribution

Names future-frame exclusion as part of the task contract and motivates a common backbone across spatial and temporal output granularities.

Sections 1 and 3.1, physical pp.1–3 · Main paper

Argument and section order

Feature shapes and a shared alignment formulation precede individual task losses, letting the reader track which feature resolution serves each task.

Sections 3.1–3.4, pp.3–6 · Main paper

Figures, captions and visual explanation

The overview maps one snowmobiling video to global language, temporal intervals and spatial masks, using the same visual/text token colors across all three levels.

Figure 2, p.4 · Main paper

Tables, comparisons and evidence

The task-mixture table retains competing optima across downstream tasks; a separate causal-versus-bidirectional comparison exposes the small accuracy cost before the latency/memory curves.

Tables 4–6 and Figure 3, p.8 · Main paper

Appendix and reproducibility

Prompt templates and full VideoQA training details are delegated to supplementary material, while the main text identifies frozen-backbone and task-head choices.

Sections 3.3 and 4.3.3, pp.4,7 · Main paper

Clarity and writing conventions

The data-efficiency discussion explicitly calls out the tradeoff between computation and annotation cost rather than treating fewer richly labelled videos as universally cheaper data.

Section 4.4, p.8 · Main paper

What to reuse—and what not to infer

Transferable lesson: Define online information access first, then separate representation training, task-head adaptation and end-to-end response cost.

Boundary: Causal attention with a KV cache does not imply bounded memory for indefinitely long streams; the plotted memory still grows. Backbone-only timing excludes task heads/LLMs, and the rich-annotation data comparison is not a matched annotation-budget study.

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-oriented scan across the main manuscript, physical PDF pp.1–8, covering framing, section progression, method explanations, captions, comparison protocols and concluding scope; reference boundary checked. Selected full-page rasters were inspected. Not exhaustive line-by-line reading, proof verification or experimental reproduction.

Appendix / supplement

Main-paper appendix/supplement references were checked. A separate supplementary source is linked in the inventory but was not retrieved or inspected; its contents are not included in the reading or visual-page counts.

Visually inspected PDF pages

4, 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.

• Official CVF open-access ICCV 2025 accepted-paper PDF, retrieved 7 October 2026; CVF states identical to accepted version except watermark; not assumed identical to initial submission · 13 pages