---
license: cc-by-nc-sa-4.0
task_categories:
- video-text-to-text
language:
- en
tags:
- video
- eccv-2026
- audiovisual-captioning
pretty_name: TCA-Bench
---

# TCA-Bench

Official ECCV 2026 benchmark release for the paper "Temporal and Cross-modal Alignment for Enhanced Audiovisual Video Captioning."

TCA-Bench is a diagnostic benchmark for audiovisual video captioning. It evaluates base audio/visual perception, audio-visual binding, and cross-modal temporal reasoning using structured ground truth annotations.

The benchmark contains 459 anonymized short videos. All annotation files use the anonymized mp4 filename as `id`, matching files in `videos/`.

## Files

- `videos.tar.gz`: compressed archive containing anonymized video files named `tca_bench_000001.mp4` through `tca_bench_000459.mp4`.
- `gt/captions.json`: Stage-1 ground-truth captions.
- `gt/stage-2.json`: Stage-2 audio-visual binding lists.
- `gt/stage-3.json`: Stage-3 temporal relation lists.
- `scripts/evaluate.py`: evaluation script.
- `scripts/prompts/`: captioning and judge prompts.
- `requirements.txt`: evaluator dependencies.
- `source_manifest.csv`: source URL and temporal segment for each anonymized video.

## Evaluation

Stage 1 evaluates base perception against `gt/captions.json`:

- `1v`: visual quality.
- `1a`: audio quality.

Stage 2 evaluates audio-visual binding against `gt/stage-2.json`.

Stage 3 evaluates temporal reasoning against `gt/stage-3.json` and reports Temporal F1 with precision and coverage.

`scripts/prompts/caption_prompt.txt` is the recommended prompt for generating candidate captions.

Extract `videos.tar.gz` in the repository root before reading video files:

```bash
tar -xzf videos.tar.gz
```

Candidate captions should be a JSON array or JSONL file:

```json
{"id": "tca_bench_000001.mp4", "caption": "Candidate caption text..."}
```

Run evaluation:

```bash
python -m pip install -r requirements.txt
export TCA_EVAL_API_KEY="..."
export TCA_EVAL_BASE_URL="https://openrouter.ai/api/v1"
export TCA_EVAL_MODEL="gpt-4.1"
python scripts/evaluate.py --input path/to/captions.json --name my-model --stage 1v 1a 2 3
```

Outputs are written to `results/<name>/<timestamp>/`.

## License

This release is for non-commercial research use under CC BY-NC-SA 4.0.

## Citation

```bibtex
@misc{tca_bench_2026,
  title = {Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning},
  year = {2026}
}
```