10 citations · 24 across the 11 of their papers we have counts for
6 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…
PreViTS: Contrastive Pretraining with Video Tracking Supervision
Brian Chen, Ramprasaath R. Selvaraju, Shih-Fu Chang +2
Videos are a rich source for self-supervised learning (SSL) of visual representations due to the presence of natural temporal transformations of objects. However, current methods t…
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…
Joint Multimedia Event Extraction from Video and Article
Brian Chen, Xudong Lin, Christopher Thomas +5
Visual and textual modalities contribute complementary information about events described in multimedia documents. Videos contain rich dynamics and detailed unfoldings of events, w…
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…