---
title: "The Devil is in the Distributions — paper by Tian et al. (2026)"
description: "Zero-shot video captioning means describing videos without training on paired video-text data. 47 references."
authors: ["Mingkai Tian", "Guorong Li", "Yuankai Qi", "Anton van den Hengel", "Qingming Huang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.98/"
doi: "10.18653/v1/2026.findings-eacl.98"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tian-2026-devil-is-distributions-explicit-modeling/"
---
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# The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning

**Authors:** [Mingkai Tian](https://md.research.elamur.ai/free/en/authors/mingkai-tian/), Guorong Li, Yuankai Qi, [Anton van den Hengel](https://md.research.elamur.ai/free/en/authors/anton-van-den-hengel/) and Qingming Huang · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.98/) · [PDF](https://aclanthology.org/2026.findings-eacl.98.pdf) · [DOI 10.18653/v1/2026.findings-eacl.98](https://doi.org/10.18653/v1/2026.findings-eacl.98) · [OpenAlex](https://openalex.org/W7140143602)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Zero-shot video captioning means describing videos without training on paired video-text data. Current text-only approaches retrieve textual prompts through CLIP at a single level of detail, such as noun phrases or full sentences. As a result, their captions often capture one salient element and miss interactions or context. The authors instead feed the decoder cues at three levels of granularity, drawn from separate memory banks:

- frequent noun phrases;
- scene-graph triples whose nodes are enriched with attribute-bearing noun phrases, chosen by BGE similarity to the source caption;
- complete training captions.

During training, prompts are retrieved with noise-perturbed text embeddings and a decoder learns to reconstruct the caption, as in MultiCapCLIP. At inference, a category-aware retrieval step reduces redundancy among prompts while keeping them visually grounded. It groups noun phrases (classified with GPT-4) and scene graphs into categories, uses corpus priors, and applies top-p filtering.

On MSR-VTT, MSVD and VATEX, the method beats prior zero-shot systems in both in-domain and cross-domain tests. This suggests that explicitly modeling scene structure matters for this task.

## Key points
- With the same backbone, CIDEr improves over the previous best by 5.7% on MSR-VTT, 16.2% on MSVD and 3.4% on VATEX.
- In the MSR-VTT⇒MSVD transfer setting, replacing plain top-K retrieval with category-aware retrieval plus top-p filtering has two effects:
  - CIDEr rises from 34.7 to 41.4;
  - prompt redundancy falls, with Self-BLEU dropping from 0.598 to 0.475.
- Stronger encoders help considerably. Moving from CLIP (ViT/B-16) to GME-Qwen2VL-7B lifts CIDEr from 39.3 to 48.2 on MSR-VTT and from 41.4 to 62.2 on VATEX.
- Building the prompts adds about 2 ms per video on MSVD. Video encoding takes about 97 ms by comparison.
- The noun phrase and scene graph banks are truncated by frequency, so rare concepts may be underrepresented. The authors plan to use an information-gain criterion for selecting bank entries.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works

## Cite

Cite as: Mingkai Tian, Guorong Li, Yuankai Qi, Anton van den Hengel and Qingming Huang. 2026. The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tian2026devil,
  title = {{The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning}},
  author = {Mingkai Tian, Guorong Li, Yuankai Qi, Anton {van den Hengel} and Qingming Huang},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.98},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.98}
}
```

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*Updated 1 November 2026*