23 citations · 59 across the 14 of their papers we have counts for
5 papers · 1 filter
Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval
Nina Shvetsova, Brian Chen, Andrew Rouditchenko +6
Multi-modal learning from video data has seen increased attention recently as it allows to train semantically meaningful embeddings without human annotation enabling tasks like zer…
Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content
Matthew Katsaros, Kathy Yang, Lauren Fratamico
The proliferation of harmful and offensive content is a problem that many online platforms face today. One of the most common approaches for moderating offensive content online is…
Cascaded Multilingual Audio-Visual Learning from Videos
Andrew Rouditchenko, Angie Boggust, David Harwath +8
In this paper, we explore self-supervised audio-visual models that learn from instructional videos. Prior work has shown that these models can relate spoken words and sounds to vis…
Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset
Ian Palmer, Andrew Rouditchenko, Andrei Barbu +2
Visually-grounded spoken language datasets can enable models to learn cross-modal correspondences with very weak supervision. However, modern audio-visual datasets contain biases t…
Cross-Modal Discrete Representation Learning
Alexander H. Liu, SouYoung Jin, Cheng-I Jeff Lai +3
Recent advances in representation learning have demonstrated an ability to represent information from different modalities such as video, text, and audio in a single high-level emb…