2.5k citations · 6.4k across the 9 of their papers we have counts for
10 papers
CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner +3
We study the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffir…
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…
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…
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…
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,…
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…
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…
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis +6
Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in auto…
Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick +2
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. I…
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal, Piotr Dollár, Ross Girshick +6
Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and developme…