activity
20102024
most citedSemantic Segmentation using Adversarial Networks

489 citations · 1.8k across the 17 of their papers we have counts for

collaborators
Showing cs.CVShow all

19 papers · 1 filter

cs.CV2024

Unlocking Pre-trained Image Backbones for Semantic Image Synthesis

Tariq Berrada, Jakob Verbeek, Camille Couprie +1

Semantic image synthesis, i.e., generating images from user-provided semantic label maps, is an important conditional image generation task as it allows to control both the content…

cs.CV2023★ 1 cited

Guided Distillation for Semi-Supervised Instance Segmentation

Tariq Berrada, Camille Couprie, Karteek Alahari +1

Although instance segmentation methods have improved considerably, the dominant paradigm is to rely on fully-annotated training images, which are tedious to obtain. To alleviate th…

cs.CV2023★ 358 cited

Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar

Jamie Tolan, Hung-I Yang, Ben Nosarzewski +13

Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. Repeated measurement…

cs.CV2022

Unifying conditional and unconditional semantic image synthesis with OCO-GAN

Marlène Careil, Stéphane Lathuilière, Camille Couprie +1

Generative image models have been extensively studied in recent years. In the unconditional setting, they model the marginal distribution from unlabelled images. To allow for more…

cs.CV2022

Efficient conditioned face animation using frontally-viewed embedding

Maxime Oquab, Daniel Haziza, Ludovic Schwartz +5

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 rea…

cs.CV2020

Low Bandwidth Video-Chat Compression using Deep Generative Models

Maxime Oquab, Pierre Stock, Oran Gafni +9

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 de…