---
license: cc-by-4.0
task_categories:
- text-retrieval
language:
- en
tags:
- composed-video-retrieval
- composed-retrieval
- video
- multimodal
- omni
pretty_name: OmniCVR
size_categories:
- 1K<n<10K
---

# OmniCVR: A Benchmark for Omni-Composed Video Retrieval with Vision, Audio, and Text

OmniCVR is a benchmark for **omni-composed video retrieval**: given a *source
video* and a natural-language *modification instruction*, the goal is to
retrieve the *target video* from a candidate gallery. The modifications span
**vision, audio, and text** jointly.

## Dataset summary

- **5,000** evaluation queries (source / target / instruction triples).
- Each query is paired with a **2,000-candidate retrieval gallery** that always
  contains the ground-truth target.
- **16,316** unique videos in total.
- All video ids are anonymized to `omnicvr_video{N}.mp4`.

## Splits

The 5,000 queries (in their line order in `omnicvr.jsonl`) are organized into
three categories by the dominant modality of the modification:

| Rows (1-indexed) | Count | Category | Description |
|------------------|-------|----------|-------------|
| 1 – 1000 | 1000 | **audio-center** | Modifications centered on the acoustic / audio content. |
| 1001 – 2141 | 1141 | **visual-center** | Modifications centered on the visual content. |
| 2142 – 5000 | 2858 | **Integrated** | Integrated modifications fusing vision, audio, and text. |

## Files

| File | Description |
|------|-------------|
| `omnicvr.jsonl` | Main annotations. One JSON object per line. |
| `videos/omnivideos-*.tar` | Sharded video archives (extract into a flat `videos/` folder). |

### `omnicvr.jsonl` schema

```json
{
  "source_id": "omnicvr_video1330.mp4",
  "target_id": "omnicvr_video1331.mp4",
  "instruction": "Maintain the ... Replace the action of ...",
  "candidates": ["omnicvr_video2298.mp4", "omnicvr_video2895.mp4", "...2000 ids..."]
}
```

- `source_id` — the query (reference) video.
- `target_id` — the ground-truth video to retrieve (always inside `candidates`).
- `instruction` — the textual modification describing source → target.
- `candidates` — the 2,000-video retrieval gallery for this query.

## Gallery construction

Each 2,000-candidate gallery contains the target, the source, up to **2 hard
distractors** (other temporal segments of the *same* underlying video, where
applicable), and the remainder sampled from the corresponding video pool.
The audio-centric split uses a single shared 2,000-video pool.

## Usage

```python
import json

# Load annotations
with open("omnicvr.jsonl") as f:
    data = [json.loads(line) for line in f]

ex = data[0]
print(ex["source_id"], ex["target_id"])
print(ex["instruction"])
print(len(ex["candidates"]))   # 2000

# Videos: download and extract the tar shards into ./videos/
#   cat videos/omnivideos-*.tar | tar -xf - -C videos/   (or extract each shard)
# Then each id maps to videos/<id>  (ids already include the .mp4 extension)
```

## Citation

```bibtex
@inproceedings{
ji2026omnicvr,
title={Omni{CVR}: A Benchmark for Omni-Composed Video Retrieval with Vision, Audio, and Text},
author={Junyang Ji and Shengjun Zhang and Da Li and Yuxiao Luo and Yan Wang and Di Xu and Biao Yang and Wei Yuan and Fan Yang and Zhihai He and Wenming Yang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=KxxR7emO5K}
}
```