2k citations · 4.7k across the 40 of their papers we have counts for
6 papers · 2 filters
Learning Video Representations from Large Language Models
Yue Zhao, Ishan Misra, Philipp Krähenbühl +1
We introduce LaViLa, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual…
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Pose
Yang Fu, Ishan Misra, Xiaolong Wang
We propose a generalizable neural radiance fields - MonoNeRF, that can be trained on large-scale monocular videos of moving in static scenes without any ground-truth annotations of…
OmniMAE: Single Model Masked Pretraining on Images and Videos
Rohit Girdhar, Alaaeldin El-Nouby, Mannat Singh +3
Transformer-based architectures have become competitive across a variety of visual domains, most notably images and videos. While prior work studies these modalities in isolation,…
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…
Omnivore: A Single Model for Many Visual Modalities
Rohit Girdhar, Mannat Singh, Nikhila Ravi +3
Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we prop…
Detecting Twenty-thousand Classes using Image-level Supervision
Xingyi Zhou, Rohit Girdhar, Armand Joulin +2
Current object detectors are limited in vocabulary size due to the small scale of detection datasets. Image classifiers, on the other hand, reason about much larger vocabularies, a…