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Found 8 papers, 4 papers with code
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.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Reconstructing Hand-Object Interactions in the Wild
In this work we explore reconstructing hand-object interactions in the wild.
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.
Designing Network Design Spaces
In this work, we present a new network design paradigm.
Ranked #378 on
Image Classification
on ImageNet
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.
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".
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.
