1.1k citations · 1.1k across the 5 of their papers we have counts for
5 papers
In Pursuit of Pixel Supervision for Visual Pre-training
Lihe Yang, Shang-Wen Li, Yang Li +5
At the most basic level, pixels are the source of the visual information through which we perceive the world. Pixels contain information at all levels, ranging from low-level attri…
Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
Dong Wang, Yang Li, Ansong Ni +12
Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sensitive. Many such generation task…
GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis
Hu Xu, Yang Jingling, Jia Sihan +2
Generative models based on deep learning have shown significant potential in medical imaging, particularly for modality transformation and multimodal fusion in MRI-based brain imag…
DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment
Cijo Jose, Théo Moutakanni, Dahyun Kang +11
Self-supervised visual foundation models produce powerful embeddings that achieve remarkable performance on a wide range of downstream tasks. However, unlike vision-language models…
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…