Search Results for author:
Found 9 papers, 4 papers with code
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
Code Translation with Compiler Representations
Here we propose to augment code translation with IRs, specifically LLVM IR, with results on the C++, Java, Rust, and Go languages.
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data.
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension
The method learns, in an end-to-end fashion, a soft partition of a given category-specific 3D template mesh into rigid parts together with a monocular reconstruction network that predicts the part motions such that they reproject correctly onto 2D DensePose-like surface annotations of the object.
Discovering Relationships between Object Categories via Universal Canonical Maps
Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects.
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i. e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them.
Unsupervised Learning of 3D Object Categories from Videos in the Wild
Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D.
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.
Continuous Surface Embeddings
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories.
