3 citations · 7 across the 5 of their papers we have counts for
6 papers
AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition
Andrew Rouditchenko, Ronan Collobert, Tatiana Likhomanenko
Audio-visual speech contains synchronized audio and visual information that provides cross-modal supervision to learn representations for both automatic speech recognition (ASR) an…
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
Brian Chen, Andrew Rouditchenko, Kevin Duarte +10
Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data…
AVLnet: Learning Audio-Visual Language Representations from Instructional Videos
Andrew Rouditchenko, Angie Boggust, David Harwath +11
Current methods for learning visually grounded language from videos often rely on text annotation, such as human generated captions or machine generated automatic speech recognitio…
Label-efficient audio classification through multitask learning and self-supervision
Tyler Lee, Ting Gong, Suchismita Padhy +2
While deep learning has been incredibly successful in modeling tasks with large, carefully curated labeled datasets, its application to problems with limited labeled data remains a…
Self-Supervised Audio-Visual Co-Segmentation
Andrew Rouditchenko, Hang Zhao, Chuang Gan +2
Segmenting objects in images and separating sound sources in audio are challenging tasks, in part because traditional approaches require large amounts of labeled data. In this pape…
The Sound of Pixels
Hang Zhao, Chuang Gan, Andrew Rouditchenko +3
We introduce PixelPlayer, a system that, by leveraging large amounts of unlabeled videos, learns to locate image regions which produce sounds and separate the input sounds into a s…