1 citations · 1 across the 6 of their papers we have counts for
6 papers
Recurrent Video Masked Autoencoders
Daniel Zoran, Nikhil Parthasarathy, Yi Yang +3
We present Recurrent Video Masked-Autoencoders (RVM): a novel approach to video representation learning that leverages recurrent computation to model the temporal structure of vide…
Unique Lives, Shared World: Learning from Single-Life Videos
Tengda Han, Sayna Ebrahimi, Dilara Gokay +8
We introduce the "single-life" learning paradigm, where we train a distinct vision model exclusively on egocentric videos captured by one individual. We leverage the multiple viewp…
Learning from Streaming Video with Orthogonal Gradients
Tengda Han, Dilara Gokay, Joseph Heyward +6
We address the challenge of representation learning from a continuous stream of video as input, in a self-supervised manner. This differs from the standard approaches to video lear…
Scaling 4D Representations
João Carreira, Dilara Gokay, Michael King +32
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video. However, prior work has focused evaluations on semantic-related tasks $\unicode{x20…
Moving Off-the-Grid: Scene-Grounded Video Representations
Sjoerd van Steenkiste, Daniel Zoran, Yi Yang +13
Current vision models typically maintain a fixed correspondence between their representation structure and image space. Each layer comprises a set of tokens arranged "on-the-grid,"…
Learning from One Continuous Video Stream
João Carreira, Michael King, Viorica Pătrăucean +9
We introduce a framework for online learning from a single continuous video stream -- the way people and animals learn, without mini-batches, data augmentation or shuffling. This p…