activity
20182022
most citedSelf-supervised Pretraining of Visual Features in the Wild

139 citations · 189 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV20222 cited

A Self-Supervised Descriptor for Image Copy Detection

Ed Pizzi, Sreya Dutta Roy, Sugosh Nagavara Ravindra +2

Image copy detection is an important task for content moderation. We introduce SSCD, a model that builds on a recent self-supervised contrastive training objective. We adapt this m…

cs.CV202248 cited

Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision

Priya Goyal, Quentin Duval, Isaac Seessel +5

Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the…

cs.CV2022

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

Priya Goyal, Adriana Romero Soriano, Caner Hazirbas +2

Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can…

cs.CV2021139 cited

Self-supervised Pretraining of Visual Features in the Wild

Priya Goyal, Mathilde Caron, Benjamin Lefaudeux +8

Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment,…

cs.CV2020

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Mathilde Caron, Ishan Misra, Julien Mairal +3

Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contr…

cs.CV2019

Scaling and Benchmarking Self-Supervised Visual Representation Learning

Priya Goyal, Dhruv Mahajan, Abhinav Gupta +1

Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learnin…