Loading Now

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)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

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