Visual question answering (VQA) with knowledge is a task that requires knowledge to answer questions on images/video. This additional requirement of knowledge poses an interesting challenge on top of the classic VQA tasks. Specifically, a system needs to explore external knowledge sources to answer the questions correctly, as well as understanding the visual content.
Representation of videos has been a major research topic for various deep learning applications including visual question answering. This is a challenging problem especially for tasks that involves vision and language and some researchers pointed out that deep neural network-based models mainly use natural language text but not the vision. We propose to use textual representation of videos, in which SOTA models for detection/recognition are used for generating text together with some rules. The results are presented at ECCV 2020.
We also work on question answering on art, which requires high-level understanding of paintings themselves as well as associated knowledge on them.
- Noa Garcia, Chentao Ye, Zihua Liu, Qingtao Hu, Mayu Otani, Chenhui Chu, Yuta Nakashima, and Teruko Mitamura (2020). A Dataset and Baselines for Visual Question Answering on Art. Proc. European Computer Vision Conference Workshops.
- Noa Garcia and Yuta Nakashima (2020). Knowledge-Based VideoQA with Unsupervised Scene Descriptions. Proc. European Conference on Computer Vision.
- Noa Garcia, Mayu Otani, Chenhui Chu, and Yuta Nakashima (2020). KnowIT VQA: Answering knowledge-based questions about videos. Proc. AAAI Conference on Artificial Intelligence.
- Zekun Yang, Noa Garcia, Chenhui Chu, Mayu Otani, Yuta Nakashima, and Haruo Takemura (2020). BERT representations for video question answering. Proc. IEEE Winter Conference on Applications of Computer Vision.
- Noa Garcia, Chenhui Chu, Mayu Otani, and Yuta Nakashima (2019). Video meets knowledge in visual question answering. MIRU.
- Zekun Yang, Noa Garcia, Chenhui Chu, Mayu Otani, Yuta Nakashima, and Haruo Takemura (2019). Video question answering with BERT. MIRU.
- BERT representations for video question answering
- KnowIT VQA: Answering knowledge-based questions about videos
- ContextNet: representation and exploration for painting classification and retrieval in context
- A comparative study of language Transformers for video question answering
- Video meets knowledge in visual question answering