Summary of Towards Multilingual Audio-visual Question Answering, by Orchid Chetia Phukan et al.
Towards Multilingual Audio-Visual Question Answering
by Orchid Chetia Phukan, Priyabrata Mallick, Swarup Ranjan Behera, Aalekhya Satya Narayani, Arun Balaji Buduru, Rajesh Sharma
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MERA framework leverages state-of-the-art video, audio, and textual foundation models to extend Audio-Visual Question Answering (AVQA) capabilities to multiple languages. The researchers create two multilingual AVQA datasets for eight languages by leveraging machine translation, eliminating the need for manual annotation efforts. To benchmark these datasets, they propose three model architectures: MERA-L, MERA-C, and MERA-T. This work has the potential to open new research directions and serve as a reference benchmark for future studies in multilingual AVQA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding what’s happening in videos and answering questions about them. Right now, this technology only works well with English videos. The researchers want to make it work with videos in many different languages. They do this by using machines to translate the video and then testing how well the computer can answer questions about the video in different languages. |
Keywords
» Artificial intelligence » Question answering » Translation
