1.1k citations · 1.7k across the 22 of their papers we have counts for
6 papers · 1 filter
Demystifying CLIP Data
Hu Xu, Saining Xie, Xiaoqing Ellen Tan +7
Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative mode…
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya +10
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance. While these components lead to effect…
Diffusion Models as Masked Autoencoders
Chen Wei, Karttikeya Mangalam, Po-Yao Huang +7
There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representati…
DINOv2: Learning Robust Visual Features without Supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni +23
The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. Thes…
STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition
Xiaoyu Zhu, Po-Yao Huang, Junwei Liang +2
We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton…
CiT: Curation in Training for Effective Vision-Language Data
Hu Xu, Saining Xie, Po-Yao Huang +5
Large vision-language models are generally applicable to many downstream tasks, but come at an exorbitant training cost that only large institutions can afford. This paper trades g…