64 citations · 98 across the 8 of their papers we have counts for
6 papers · 1 filter
ImageBind: One Embedding Space To Bind Them All
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu +4
We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. We show that all combinations of p…
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
Xudong Wang, Rohit Girdhar, Stella X. Yu +1
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'disc…
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities
Melissa Hall, Laura Gustafson, Aaron Adcock +2
We explore the extent to which zero-shot vision-language models exhibit gender bias for different vision tasks. Vision models traditionally required task-specific labels for repres…
A Simple Recipe for Competitive Low-compute Self supervised Vision Models
Quentin Duval, Ishan Misra, Nicolas Ballas
Self-supervised methods in vision have been mostly focused on large architectures as they seem to suffer from a significant performance drop for smaller architectures. In this pape…
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…
Mask2Former for Video Instance Segmentation
Bowen Cheng, Anwesa Choudhuri, Ishan Misra +3
We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline. In this re…