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Found 8 papers, 2 papers with code
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar
The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps.
DINOv2: Learning Robust Visual Features without Supervision
The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
Ranked #1 on
Image Classification
on CIFAR-10
Efficient conditioned face animation using frontally-viewed embedding
As the quality of few shot facial animation from landmarks increases, new applications become possible, such as ultra low bandwidth video chat compression with a high degree of realism.
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages.
Ranked #3 on
Speech Recognition
on Common Voice French
(using extra training data)
ROMUL: Scale Adaptative Population Based Training
In most pragmatic settings, data augmentation and regularization are essential, and require hyperparameter search.
Low Bandwidth Video-Chat Compression using Deep Generative Models
To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network.
Population Based Training for Data Augmentation and Regularization in Speech Recognition
It compares favorably to a baseline that does not change those hyperparameters over the course of training, with an 8% relative WER improvement.
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning
This article shows how the recent breakthroughs in Reinforcement Learning (RL) that have enabled robots to learn to play arcade video games, walk or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems.
