[IPOL announce] new article: A Brief Analysis of SLAVC method for Sound Source Localization

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2024/525/


Xavier Juanola, and Gloria Haro,
A Brief Analysis of SLAVC method for Sound Source Localization,
Image Processing On Line, 14 (2024), pp. 159–172.
https://doi.org/10.5201/ipol.2024.525


Abstract
Mo and Morgado introduced in 2022 a novel self-supervised learning 
approach for Visual Sound Source Localization, denoted as SLAVC [Mo, S. 
and Mordado, P., A Closer Look at Weakly-Supervised Audio-Visual Source 
Localization, Advances in Neural Information Processing Systems, 2022]. 
The proposed method is based on multiple-instance contrastive learning. 
In addition to improving the results of previous methods, it also solves 
two critical problems that former methods faced: 1) excessive 
overfitting despite training on extensive datasets, 2) tendency to 
hallucinate sound sources even without visual evidence to support it in 
the video. In this paper, we briefly present the method, offer an online 
executable version allowing the users to test it on their own 
image-audio pairs and propose some improvements that could benefit the 
model as future work.