evalstone/evalscope/docs/en/benchmarks/mvbench.md

MVBench

Overview

MVBench is a public multimodal video understanding benchmark covering temporal perception, attribute/state reasoning, symbolic ordering, and high-level cognition. This native adapter uses the ModelScope PKU-Alignment/MVBench mirror by default, which provides JSON annotations plus optimized video archives.

Task Description

• Task Type: Video multiple-choice question answering
• Input: Video + question + answer choices
• Output: Single correct answer letter
• Subsets: 20 MVBench tasks; the default smoke-test subset is action_antonym

Evaluation Notes

• Default configuration uses 0-shot evaluation
• Primary metric: Accuracy
• The default action_antonym subset downloads a small public MP4 archive for quick validation
• Full benchmark evaluation can be requested by setting subset_list to additional MVBench subsets
• Time-bounded records keep start/end metadata and add a short segment instruction to the prompt

Properties

Property	Value
Benchmark Name	mvbench
Dataset ID	PKU-Alignment/MVBench
Paper	Paper
Tags	MCQ, MultiModal, Video
Metrics	accuracy
Default Shots	0-shot
Evaluation Split	train

Data Statistics

Metric	Value
Total Samples	4,000

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
action_antonym	200	N/A	N/A	N/A
action_count	200	N/A	N/A	N/A
action_localization	200	N/A	N/A	N/A
action_prediction	200	N/A	N/A	N/A
action_sequence	200	N/A	N/A	N/A
character_order	200	N/A	N/A	N/A
counterfactual_inference	200	N/A	N/A	N/A
egocentric_navigation	200	N/A	N/A	N/A
episodic_reasoning	200	N/A	N/A	N/A
fine_grained_action	200	N/A	N/A	N/A
fine_grained_pose	200	N/A	N/A	N/A
moving_attribute	200	N/A	N/A	N/A
moving_count	200	N/A	N/A	N/A
moving_direction	200	N/A	N/A	N/A
object_existence	200	N/A	N/A	N/A
object_interaction	200	N/A	N/A	N/A
object_shuffle	200	N/A	N/A	N/A
scene_transition	200	N/A	N/A	N/A
state_change	200	N/A	N/A	N/A
unexpected_action	200	N/A	N/A	N/A

Sample Example

Sample example not available.

Prompt Template

Prompt Template:

Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.

{question}

{choices}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets mvbench \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['mvbench'],
    dataset_args={
        'mvbench': {
            # subset_list: ['action_antonym', 'action_count', 'action_localization']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)