35 citations · 140 across the 13 of their papers we have counts for
16 papers
Conditional Object-Centric Learning from Video
Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran +6
Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built. Rec…
SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping
Austin Stone, Daniel Maurer, Alper Ayvaci +2
We present SMURF, a method for unsupervised learning of optical flow that improves state of the art on all benchmarks by to (over the prior best method UFlow) and eve…
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar +5
General-purpose robotic systems must master a large repertoire of diverse skills to be useful in a range of daily tasks. While reinforcement learning provides a powerful framework…
Adaptive Intermediate Representations for Video Understanding
Juhana Kangaspunta, AJ Piergiovanni, Rico Jonschkowski +2
A common strategy to video understanding is to incorporate spatial and motion information by fusing features derived from RGB frames and optical flow. In this work, we introduce a…
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels
Austin Stone, Oscar Ramirez, Kurt Konolige +1
Robots have to face challenging perceptual settings, including changes in viewpoint, lighting, and background. Current simulated reinforcement learning (RL) benchmarks such as DM C…
Learning Object-Centric Video Models by Contrasting Sets
Sindy Löwe, Klaus Greff, Rico Jonschkowski +2
Contrastive, self-supervised learning of object representations recently emerged as an attractive alternative to reconstruction-based training. Prior approaches focus on contrastin…