video_understanding/README.md

FCMBench — Video Understanding Track Evaluation

🌐 简体中文

This repository provides evaluation scripts for FCMBench-Video (Video Understanding track). The workflow is:

1. prepare the video data and instruction file
2. run inference with your model to produce a JSONL prediction file
3. evaluate predictions against the provided ground-truth (or submit results for leaderboard ranking)

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Test Data Composition

FCMBench-Video v1.0 contains 11,322 video–question pairs across 7 tasks and 2 language settings.

Each video is a handheld recording of credit-review documents (2—4 per clip) at 20s / 40s / 60s, with 3 takes per duration. The 1,200 videos (≈135 person-scenarios) are organized as:

FCMBench-Video_v1.0_Videos/
└── video/
    ├── Construction-cn/       # CN original videos (15 persons × 9 = 135 clips)
    ├── Construction-en-US/    # EN original videos (30 persons × 9 = 265 clips)
    ├── VPI-cn/                # CN videos with visual prompt injection (135 clips)
    ├── VPI-cot-cn/            # CN VPI + Chain-of-Thought (135 clips)
    ├── VPI-en-US/             # EN VPI (265 clips)
    └── VPI-cot-en-US/         # EN VPI + CoT (265 clips)

Instruction files

File	Description	Has GT	Use case
FCMBench-Video_v1.0_full.jsonl	Full 11,322 samples (CN + EN merged)	✗	Public release; run inference
FCMBench-Video_v1.0_small.jsonl	Stratified 10% sample (~1,135)	✓	Quick self-evaluation / sanity check

Tasks (7 types across perception & reasoning):

Category	Task	CN (zh_zh / zh_en)	EN (en_en)
perception	classification	1,350	1,325
perception	counting	1,350	1,325
perception	temporal_grounding	1,350	1,325
reasoning	VPI	540	530
reasoning	VPI-CoT	540	530
reasoning	CDV	270	—
reasoning	EGS	560	327
Total		5,960	5,362

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Environments

• Python 3.10+
• uv is recommended for dependency management
• Or use pip if you prefer a traditional virtual environment

# uv
uv sync

# pip
pip install openai tqdm json-repair

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Quickstart

1) Prepare video data

unzip FCMBench-Video_v1.0_Videos.zip

The unzipped tree will look like:

FCMBench-Video_v1.0_Videos/
└── video/
    ├── Construction-cn/
    ├── Construction-en-US/
    ├── VPI-cn/
    ├── VPI-cot-cn/
    ├── VPI-en-US/
    └── VPI-cot-en-US/

The instruction JSONL files use video_prefix + video_path fields (e.g. "video_prefix": "Construction-cn", "video_path": "yangyimiao/yangyimiao_20s_1.mp4"), so point --video_root to the FCMBench-Video_v1.0_Videos/ directory.

2) Run inference

Use the single instruction file FCMBench-Video_v1.0_full.jsonl (no ground-truth). Both the Python script and the shell pipeline accept a --input_file argument.

bash benchmark_pipeline.sh \
  --input_file FCMBench-Video_v1.0_full.jsonl \
  --output_dir ./results \
  --video_root ./FCMBench-Video_v1.0_Videos \
  --model <model_name> \
  --base_url <base_url>

Or call benchmark_infer.py directly:

python benchmark_infer.py \
  --input_file FCMBench-Video_v1.0_full.jsonl \
  --output_dir ./results \
  --video_root ./FCMBench-Video_v1.0_Videos \
  --model <model_name> \
  --base_url <base_url>

Resume support: add --resume to skip task IDs already present in the output file.

The inference script writes one result file:

results/FCMBench-Video_v1.0_full_<model>_<run_id>.jsonl

Each output line is the original instruction line plus a "response" field containing the model's raw reply.

3) Evaluate predictions

python benchmark_eval.py --result_dir ./results

The evaluator expects exactly one .jsonl file in --result_dir. It prints per-task metrics (by zh/en subset and by video duration) to stdout and also writes:

• results/eval_reports/FCMBench-Video_v1.0_full_<model>_<run_id>.txt — per-task breakdown
• results/eval_reports/benchmark_overall.txt — benchmark-level overall score

Self-evaluation with small.jsonl

For a quick sanity check, use FCMBench-Video_v1.0_small.jsonl (~1,135 stratified samples with ground-truth). Run inference on it (same workflow as above), then evaluate — metrics are computed against the included GT.

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Leaderboard

Self-assessment (small.jsonl)

Researchers can run inference on FCMBench-Video_v1.0_small.jsonl and compute evaluation metrics locally using benchmark_eval.py. This gives a reliable approximation of model performance across all 7 tasks and both language subsets.

Official leaderboard submission

To have your model ranked on the FCMBench-Video leaderboard:

1. Run inference on FCMBench-Video_v1.0_full.jsonl with your model.
2. Save predictions to a single JSONL file (the benchmark_infer.py output format is the expected format — one JSON object per line with task_id and response).
3. Email the result file to yangyehuisw@126.com with the following information:
   • Model name / version
   • Inference framework (or API) and key settings (e.g., temperature, max tokens)
   • Any special post-processing applied (if applicable)

After validation, we will compute the official metrics on the hidden ground-truth and update the leaderboard.