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Found 15 papers, 8 papers with code
Designing Network Design Spaces
In this work, we present a new network design paradigm.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Data Distillation: Towards Omni-Supervised Learning
We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
On Network Design Spaces for Visual Recognition
Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape.
Learning to Learn with Generative Models of Neural Network Checkpoints
We explore a data-driven approach for learning to optimize neural networks.
Masked Visual Pre-training for Motor Control
This paper shows that self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels.
Real-World Robot Learning with Masked Visual Pre-training
Finally, we train a 307M parameter vision transformer on a massive collection of 4. 5M images from the Internet and egocentric videos, and demonstrate clearly the benefits of scaling visual pre-training for robot learning.
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as "single-tasking multiple tasks".
State-Only Imitation Learning for Dexterous Manipulation
To tackle this setting, we train an inverse dynamics model and use it to predict actions for state-only demonstrations.
Reconstructing Hand-Object Interactions in the Wild
In this work we explore reconstructing hand-object interactions in the wild.
Learning to Imitate Object Interactions from Internet Videos
In this paper we make two main contributions: (1) a novel reconstruction technique RHOV (Reconstructing Hands and Objects from Videos), which reconstructs 4D trajectories of both the hand and the object using 2D image cues and temporal smoothness constraints; (2) a system for imitating object interactions in a physics simulator with reinforcement learning.
Real-World Humanoid Locomotion with Reinforcement Learning
Humanoid robots that can autonomously operate in diverse environments have the potential to help address labour shortages in factories, assist elderly at homes, and colonize new planets.
Robot Learning with Sensorimotor Pre-training
We present a self-supervised sensorimotor pre-training approach for robotics.
Reconstructing Hands in 3D with Transformers
The key to HaMeR's success lies in scaling up both the data used for training and the capacity of the deep network for hand reconstruction.
Humanoid Locomotion as Next Token Prediction
We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language.
