Datasets:

FunAILab
/

TVBench

---
language:
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- visual-question-answering
modalities:
- Video
- Text
configs:
- config_name: action_antonym
  data_files:
  - split: train
    path: action_antonym/train-*
- config_name: action_count
  data_files:
  - split: train
    path: action_count/train-*
- config_name: action_localization
  data_files:
  - split: train
    path: action_localization/train-*
- config_name: action_sequence
  data_files:
  - split: train
    path: action_sequence/train-*
- config_name: egocentric_sequence
  data_files: json/egocentric_sequence.json
- config_name: moving_direction
  data_files: json/moving_direction.json
- config_name: object_count
  data_files: json/object_count.json
- config_name: object_shuffle
  data_files: json/object_shuffle.json
- config_name: scene_transition
  data_files: json/scene_transition.json
- config_name: unexpected_action
  data_files: json/unexpected_action.json
dataset_info:
- config_name: action_antonym
  features:
  - name: video
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: candidates
    sequence: string
  - name: video_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 51780
    num_examples: 320
  download_size: 6963
  dataset_size: 51780
- config_name: action_count
  features:
  - name: video
    dtype: string
  - name: question
    dtype: string
  - name: candidates
    sequence: string
  - name: answer
    dtype: string
  splits:
  - name: train
    num_bytes: 72611
    num_examples: 536
  download_size: 6287
  dataset_size: 72611
- config_name: action_localization
  features:
  - name: video
    dtype: string
  - name: question
    dtype: string
  - name: candidates
    sequence: string
  - name: answer
    dtype: string
  - name: start
    dtype: float64
  - name: end
    dtype: float64
  - name: accurate_start
    dtype: float64
  - name: accurate_end
    dtype: float64
  splits:
  - name: train
    num_bytes: 47290
    num_examples: 160
  download_size: 12358
  dataset_size: 47290
- config_name: action_sequence
  features:
  - name: video
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: candidates
    sequence: string
  - name: question_id
    dtype: string
  - name: start
    dtype: float64
  - name: end
    dtype: float64
  splits:
  - name: train
    num_bytes: 67660
    num_examples: 437
  download_size: 13791
  dataset_size: 67660
---

<div align="center">

<h1><a style="color:blue" href="https://daniel-cores.github.io/tvbench/">TVBench: Redesigning Video-Language Evaluation</a></h1>

[Daniel Cores](https://scholar.google.com/citations?user=pJqkUWgAAAAJ)\*,
[Michael Dorkenwald](https://scholar.google.com/citations?user=KY5nvLUAAAAJ)\*,
[Manuel Mucientes](https://scholar.google.com.vn/citations?user=raiz6p4AAAAJ),
[Cees G. M. Snoek](https://scholar.google.com/citations?user=0uKdbscAAAAJ),
[Yuki M. Asano](https://scholar.google.co.uk/citations?user=CdpLhlgAAAAJ)

*Equal contribution.
[![arXiv](https://img.shields.io/badge/cs.CV-2410.07752-b31b1b?logo=arxiv&logoColor=red)](https://arxiv.org/abs/2410.07752)
[![GitHub](https://img.shields.io/badge/GitHub-TVBench-blue?logo=github)](https://github.com/daniel-cores/tvbench)
[![Static Badge](https://img.shields.io/badge/website-TVBench-8A2BE2)](https://daniel-cores.github.io/tvbench/)

</div>

### Updates
- <h4 style="color:red">25 October 2024: Revised annotations for Action Sequence and removed duplicate samples for Action Sequence and Unexpected Action.</h4>

# TVBench
TVBench is a new benchmark specifically created to evaluate temporal understanding in video QA. We identified three main issues in existing datasets: (i) static information from single frames is often sufficient to solve the tasks (ii) the text of the questions and candidate answers is overly informative, allowing models to answer correctly without relying on any visual input (iii) world knowledge alone can answer many of the questions, making the benchmarks a test of knowledge replication rather than visual reasoning. In addition, we found that open-ended question-answering benchmarks for video understanding suffer from similar issues while the automatic evaluation process with LLMs is unreliable, making it an unsuitable alternative.

We defined 10 temporally challenging tasks that either require repetition counting (Action Count), properties about moving objects (Object Shuffle, Object Count, Moving Direction), temporal localization (Action Localization, Unexpected Action), temporal sequential ordering (Action Sequence, Scene Transition, Egocentric Sequence) and distinguishing between temporally hard Action Antonyms such as "Standing up" and "Sitting down".

In TVBench, state-of-the-art text-only, image-based, and most video-language models perform close to random chance, with only the latest strong temporal models, such as Tarsier, outperforming the random baseline. In contrast to MVBench, the performance of these temporal models significantly drops when videos are reversed.

![image](figs/fig1.png)

### Dataset statistics:
The table below shows the number of samples and the average frame length for each task in TVBench.

<center>
<img src="figs/tvbench_stats.png" alt="drawing" width="400"/>
</center>

## Download
Question and answers are provided as a json file for each task.

Videos in TVBench are sourced from Perception Test, CLEVRER, STAR, MoVQA, Charades-STA, NTU RGB+D, FunQA and CSV. All videos are included in this repository, except for those from NTU RGB+D, which can be downloaded from the official [website](https://rose1.ntu.edu.sg/dataset/actionRecognition/). It is not necessary to download the full dataset, as NTU RGB+D provides a subset specifically for TVBench with the required videos. These videos are required by th Action Antonym task and should be stored in the `video/action_antonym` folder.

## Leaderboard
![image](figs/sota.png)

# Citation
If you find this benchmark useful, please consider citing:
```

@misc{cores2024tvbench,
  author = {Daniel Cores and Michael Dorkenwald and Manuel Mucientes and Cees G. M. Snoek and Yuki M. Asano},
  title = {TVBench: Redesigning Video-Language Evaluation},
  year = {2024},
  eprint = {arXiv:2410.07752},
}

```