no code implementations • 7 Mar 2024 • Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner, Priya Goyal, Alexander D'Amour, Xiaohua Zhai
Interestingly, data and architectural improvements seem to mitigate the negative impact of data balancing on performance; e. g. applying M4 to SigLIP-B/16 with data quality filters improves COCO image-to-text retrieval @5 from 86% (without data balancing) to 87% and ImageNet 0-shot classification from 77% to 77. 5%!
1 code implementation • CVPR 2022 • Ed Pizzi, Sreya Dutta Roy, Sugosh Nagavara Ravindra, Priya Goyal, Matthijs Douze
We adapt this method to the copy detection task by changing the architecture and training objective, including a pooling operator from the instance matching literature, and adapting contrastive learning to augmentations that combine images.
1 code implementation • 16 Feb 2022 • Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Ishan Misra, Levent Sagun, Armand Joulin, Piotr Bojanowski
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images.
Ranked #1 on Copy Detection on Copydays strong subset (using extra training data)
1 code implementation • 15 Feb 2022 • Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun, Nicolas Usunier
Systematic diagnosis of fairness, harms, and biases of computer vision systems is an important step towards building socially responsible systems.
1 code implementation • 2 Mar 2021 • Priya Goyal, Mathilde Caron, Benjamin Lefaudeux, Min Xu, Pengchao Wang, Vivek Pai, Mannat Singh, Vitaliy Liptchinsky, Ishan Misra, Armand Joulin, Piotr Bojanowski
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods.
Ranked #6 on Image Classification on Places205
Self-Supervised Image Classification Self-Supervised Learning +1
16 code implementations • NeurIPS 2020 • Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin
In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much.
Ranked #9 on Image Classification on OmniBenchmark
2 code implementations • ICCV 2019 • Priya Goyal, Dhruv Mahajan, Abhinav Gupta, Ishan Misra
Self-supervised learning aims to learn representations from the data itself without explicit manual supervision.
4 code implementations • 13 Feb 2018 • Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S. Moses, Sven Verdoolaege, Andrew Adams, Albert Cohen
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 automatic translation, speech-to-text, scene understanding, ranking user preferences, ad placement, etc.
230 code implementations • ICCV 2017 • Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár
Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
Ranked #3 on Region Proposal on COCO test-dev
71 code implementations • 8 Jun 2017 • Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He
To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training.