| --- |
| 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} |
| } |
| ``` |
|
|
