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https://github.com/sutdcv/SUTD-TrafficQA
[CVPR2021] SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
https://github.com/sutdcv/SUTD-TrafficQA
annotations cvpr cvpr2021 dataset multimodal multimodal-deep-learning paper traffic-events video-qa video-reasoning vqa vqa-dataset
Last synced: about 2 months ago
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[CVPR2021] SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
- Host: GitHub
- URL: https://github.com/sutdcv/SUTD-TrafficQA
- Owner: sutdcv
- Created: 2021-03-27T11:20:40.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-08-19T04:17:43.000Z (4 months ago)
- Last Synced: 2024-08-19T05:29:40.897Z (4 months ago)
- Topics: annotations, cvpr, cvpr2021, dataset, multimodal, multimodal-deep-learning, paper, traffic-events, video-qa, video-reasoning, vqa, vqa-dataset
- Language: JavaScript
- Homepage: https://sutdcv.github.io/SUTD-TrafficQA/
- Size: 6 MB
- Stars: 49
- Watchers: 4
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Citation: CITATION
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README
# [SUTD-TrafficQA](https://sutdcv.github.io/SUTD-TrafficQA/)
A challenging **Video Question Answering (VQA)** Benchmark based on **real-world** traffic scenes.
**Updates:**
- `Jul 2021` The dataset is publicly released. You may [request download](https://sutdcv.github.io/SUTD-TrafficQA/#/download) now.
- `Jun 2021` The [dataset usage details](https://sutdcv.github.io/SUTD-TrafficQA/#/explore) are available now.
- `May 2021` The dataset [homepage](https://sutdcv.github.io/SUTD-TrafficQA) is live now.
- `Feb 2021` ~~The dataset is available upon email request.~~
## Paper
Our paper at **CVPR 2021**, _SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events_, is available at: [[CVF Open Access]](https://openaccess.thecvf.com/content/CVPR2021/html/Xu_SUTD-TrafficQA_A_Question_Answering_Benchmark_and_an_Efficient_Network_for_CVPR_2021_paper.html), [[arXiv:2103.15538]](https://arxiv.org/abs/2103.15538), and [[ResearchGate]](https://www.researchgate.net/publication/350432154_TrafficQA_A_Question_Answering_Benchmark_and_an_Efficient_Network_for_Video_Reasoning_over_Traffic_Events).
## Dataset
- Annotation Example [examples/annotation_sample.jsonl](examples/annotation_sample.jsonl)
- Jsonl Reader Example [examples/jsonl_reader.py](examples/jsonl_reader.py)
- Appearance Feature Preprocessing [examples/preprocess_video_appearance_example.py](examples/preprocess_video_appearance_example.py)
- Motion Feature Preprocessing [examples/preprocess_video_motion_example.py](examples/preprocess_video_motion_example.py)
- Dataloader [examples/dataloader_example.py](examples/dataloader_example.py)
- [Download Dataset](https://sutdcv.github.io/SUTD-TrafficQA)
## Citation
```bibtex
@InProceedings{Xu_2021_CVPR,
author = {Xu, Li and Huang, He and Liu, Jun},
title = {{SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning Over Traffic Events}},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {9878-9888}
}
```
## Acknowledgment
Contributors: [**Lin Yutian**](https://github.com/Lynn-020809), [**Tran Nguyen Bao Long**](https://github.com/TNBL265), [**Liu Renhang**](https://github.com/Samillynn), [**Qiao Yingjie**](https://github.com/YingjieQiao), **Xun Long Ng**, **Koh Kai Ting**, **Christabel Dorothy**
Code Reference: [thaolmk54 / hcrn-videoqa](https://github.com/thaolmk54/hcrn-videoqa)
## Contact
- `li_xu [AT] mymail.sutd.edu.sg`
- `he_huang [AT] mymail.sutd.edu.sg`