TVBench

Overview

TVBench is a temporal video understanding benchmark for evaluating whether multimodal models can reason over dynamic visual events rather than isolated frames. It covers a broad set of video reasoning skills, including action recognition, action counting, temporal localization, action order understanding, egocentric action sequencing, object counting, object shuffling, moving direction recognition, scene transition reasoning, and unexpected action detection.

The EvalScope native adapter reads the official per-task JSON annotations from the dataset repository and resolves the corresponding video archives on demand. This keeps the default smoke-test path lightweight while still supporting the full benchmark through subset_list.

Task Description

• Task Type: Video multiple-choice question answering (MCQ)

• Input: A video clip or a time-bounded video segment, a natural-language question, and 2-4 answer candidates

• Output: A single answer option letter selected from the provided candidates

• Default Subset: action_count, selected because it is available as a standard MP4 archive in the public dataset repository

• Supported Subsets: action_antonym, action_count, action_localization, action_sequence, egocentric_sequence, moving_direction, object_count, object_shuffle, scene_transition, and unexpected_action

Evaluation Notes

• Default evaluation uses 0-shot Chain-of-Thought multiple-choice prompting via MultipleChoiceTemplate.SINGLE_ANSWER_COT.

• Primary metric: Accuracy (acc). The dataset answer is stored as candidate text and is converted to the corresponding option letter before scoring.

• Some subsets provide start/end fields. The adapter passes these values to ContentVideo and also adds a concise segment instruction to the prompt.

• Video files are downloaded lazily from subset-specific archives. egocentric_sequence uses segmented archives under video/egocentric_sequence/<prefix>.zip.

• The action_antonym annotations reference AVI files. If the repository media archive is unavailable, configure extra_params.video_dir to a local directory containing the AVI files.

• The adapter supports local video layouts that are either flat (video_dir/<video>) or grouped by subset (video_dir/<subset>/<video>).

Properties

Property	Value
Benchmark Name	tvbench
Dataset ID	evalscope/TVBench
Paper	N/A
Tags	MCQ, MultiModal
Metrics	acc
Default Shots	0-shot
Evaluation Split	train

Data Statistics

Metric	Value
Total Samples	536
Prompt Length (Mean)	326.44 chars
Prompt Length (Min/Max)	290 / 358 chars

Video Statistics:

Metric	Value
Total Videos	536
Videos per Sample	min: 1, max: 1, mean: 1
Formats	mp4

Sample Example

Subset: action_count

{
  "input": [
    {
      "id": "4b521c81",
      "content": [
        {
          "text": "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 A,B,C,D. Think step by step before answering.\n\nThe person makes sets of repeated actions. How many times did the person repeat the action in the last set?\n\nA) 2\nB) 3\nC) 5\nD) 4"
        },
        {
          "video": "~/.cache/modelscope/hub/datasets/tvbench/videos/action_count/video_8686.mp4",
          "format": "mp4"
        }
      ]
    }
  ],
  "choices": [
    "2",
    "3",
    "5",
    "4"
  ],
  "target": "D",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "video": "video_8686.mp4",
    "answer": "4",
    "subset": "action_count",
    "start": null,
    "end": null,
    "fps": null,
    "accurate_start": null,
    "accurate_end": null,
    "video_length": null,
    "question_id": null,
    "dataset_id": "evalscope/TVBench",
    "dataset_hub": "modelscope"
  }
}

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}

Extra Parameters

Parameter	Type	Default	Description
video_dir	str	``	Optional local directory containing TVBench video files. It may be organized flat or by subset.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets tvbench \
    --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=['tvbench'],
    dataset_args={
        'tvbench': {
            # extra_params: {}  # uses default extra parameters
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)