39 citations · 117 across the 18 of their papers we have counts for
10 papers · 1 filter
Emergence of Segmentation with Minimalistic White-Box Transformers
Yaodong Yu, Tianzhe Chu, Shengbang Tong +4
Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection. Previous works have shown th…
Canonical Factors for Hybrid Neural Fields
Brent Yi, Weijia Zeng, Sam Buchanan +1
Factored feature volumes offer a simple way to build more compact, efficient, and intepretable neural fields, but also introduce biases that are not necessarily beneficial for real…
ViP: A Differentially Private Foundation Model for Computer Vision
Yaodong Yu, Maziar Sanjabi, Yi Ma +2
Artificial intelligence (AI) has seen a tremendous surge in capabilities thanks to the use of foundation models trained on internet-scale data. On the flip side, the uncurated natu…
Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis
Yihan Ma, Zhengyu Zhao, Xinlei He +3
Large text-to-image models have shown remarkable performance in synthesizing high-quality images. In particular, the subject-driven model makes it possible to personalize the image…
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
Tianzhe Chu, Shengbang Tong, Tianjiao Ding +4
The advent of large pre-trained models has brought about a paradigm shift in both visual representation learning and natural language processing. However, clustering unlabeled imag…
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
Shengbang Tong, Yubei Chen, Yi Ma +1
Recently, self-supervised learning (SSL) has achieved tremendous success in learning image representation. Despite the empirical success, most self-supervised learning methods are…