Datasets:

LIQIIIII
/

ViMU

license: mit
task_categories:
  - visual-question-answering
  - question-answering
  - text-classification
language:
  - en
tags:
  - video-understanding
  - multimodal
  - video-metaphorical-understanding
  - benchmark
  - subtext-understanding
pretty_name: ViMU
configs:
  - config_name: OI
    data_files:
      - split: eval
        path: metadata/vimu_oe.jsonl
  - config_name: EG
    data_files:
      - split: eval
        path: metadata/vimu_eg.jsonl
  - config_name: RMI-SVI
    data_files:
      - split: eval
        path: metadata/vimu_ss.jsonl

ViMU: Benchmarking Video Metaphorical Understanding

Qi Li, Xinchao Wang^*

^*Corresponding author

xML Lab, National University of Singapore

This repository contains the evaluation scripts for ViMU, a benchmark for video metaphorical understanding. The code evaluates multimodal models on four tasks:

Open-ended interpretation (OE)
Evidence grounding (EG)
Rhetoric mechanism identification (RM)
Social value signal identification (SV)

Directory Structure

Expected project structure:

ViMU/
├── videos/
│   ├── vimu_000001.mp4
│   └── ...
├── metadata/
│   ├── vimu_oe.jsonl
│   ├── vimu_eg.jsonl
│   ├── vimu_ss.jsonl
│   ├── video_evidence.jsonl
│   └── cache/
├── scripts/
│   ├── 00-vimu_oe.py
│   ├── 01-vimu_oe_judge.py
│   ├── 02-vimu_oe_score.py
│   ├── 10-vimu_eg.py
│   ├── 11-vimu_eg_score.py
│   ├── 20-vimu_ss.py
│   ├── 21-vimu_ss_score.py
│   └── utils.py
└── output/

Setup

Install dependencies:

pip install openai requests numpy pandas tqdm

Depending on the models used, additional API keys may be required.

Set API keys:

export OPENAI_API_KEY="your_openai_key"
export OPENROUTER_API_KEY="your_openrouter_key"
export GOOGLE_API_KEY="your_google_key"

Not all keys are required if you only run a subset of models.

Path Configuration

Before running, edit each script and set:

PROJECT_ROOT = "/Your/Path/To/ViMU"

Recommended Running Order

For a full evaluation, run:

# Open-ended interpretation
python scripts/00-vimu_oe.py
python scripts/01-vimu_oe_judge.py
python scripts/02-vimu_oe_score.py

# Evidence grounding
python scripts/10-vimu_eg.py
python scripts/11-vimu_eg_score.py

# Structured subtext tasks without guidance
python scripts/20-vimu_ss.py --prompt_mode without_guidance
python scripts/21-vimu_ss_score.py --prompt_mode without_guidance

# Structured subtext tasks with guidance
python scripts/20-vimu_ss.py --prompt_mode with_guidance
python scripts/21-vimu_ss_score.py --prompt_mode with_guidance

Model Configuration

Models are configured in the MODEL_SPECS list inside the inference scripts.

To enable or disable a model, edit:

"enabled": True

"enabled": False

For OpenRouter models, make sure the model ID and API key are valid.

Output Files

The main output files are:

output/vimu_oe_summary.json
output/vimu_eg_summary.json
output/vimu_ss_without_guidance_summary.json
output/vimu_ss_with_guidance_summary.json

These files contain aggregated evaluation results.

Scoring Rules

Open-ended Interpretation

Open-ended answers are evaluated using an LLM-as-a-judge protocol. The judge scores semantic understanding based on:

core intent
implicit signal
target or social meaning
hallucination penalty
literal-only penalty

Evidence grounding is scored as a multi-label prediction problem. If the prediction contains any incorrect option, the score is 0. Otherwise, if the prediction is a subset of the gold answer, the score is: score = number of correctly selected options / number of gold options. Rhetoric and social value tasks use the same multi-label scoring rule. If no incorrect option is selected; otherwise: score = 0.

Notes

The dataset contains socially sensitive video memes. The benchmark is intended for research use only.

Citation

If you finding our work interesting or helpful to you, please cite as follows:

@article{li2026vimu,
  title={ViMU: Benchmarking Video Metaphorical Understanding},
  author={Li, Qi and Wang, Xinchao},
  journal={arXiv preprint arXiv:2605.14607},
  year={2026}
}