1 citations · 2 across the 7 of their papers we have counts for
7 papers
Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems
Aidan Li, Uday Kiran Reddy Tadipatri, Mahan Fathi +2
Koopman autoencoders (KAEs) seek a higher-dimensional latent representation in which nonlinear dynamics evolve linearly. However, many interesting systems have multiple basins of a…
TAPNext++: What's Next for Tracking Any Point (TAP)?
Sebastian Jung, Artem Zholus, Martin Sundermeyer +6
Tracking-Any-Point (TAP) models aim to track any point through a video which is a crucial task in AR/XR and robotics applications. The recently introduced TAPNext approach proposes…
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus, Carl Doersch, Yi Yang +7
Tracking Any Point (TAP) in a video is a challenging computer vision problem with many demonstrated applications in robotics, video editing, and 3D reconstruction. Existing methods…
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…
TRecViT: A Recurrent Video Transformer
Viorica Pătrăucean, Xu Owen He, Joseph Heyward +10
We propose a novel block for \emph{causal} video modelling. It relies on a time-space-channel factorisation with dedicated blocks for each dimension: gated linear recurrent units (…
BootsTAP: Bootstrapped Training for Tracking-Any-Point
Carl Doersch, Pauline Luc, Yi Yang +8
To endow models with greater understanding of physics and motion, it is useful to enable them to perceive how solid surfaces move and deform in real scenes. This can be formalized…
Course Correcting Koopman Representations
Mahan Fathi, Clement Gehring, Jonathan Pilault +3
Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space. Theoretically, such features can be used to s…