2 citations · 3 across the 9 of their papers we have counts for
8 papers · 1 filter
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
Zeyu Wang, Cihang Xie, Brian Bartoldson +1
This paper investigates the robustness of vision-language models against adversarial visual perturbations and introduces a novel ``double visual defense" to enhance this robustness…
CLIPS: An Enhanced CLIP Framework for Learning with Synthetic Captions
Yanqing Liu, Xianhang Li, Zeyu Wang +2
Previous works show that noisy, web-crawled image-text pairs may limit vision-language pretraining like CLIP and propose learning with synthetic captions as a promising alternative…
What If We Recaption Billions of Web Images with LLaMA-3?
Xianhang Li, Haoqin Tu, Mude Hui +9
Web-crawled image-text pairs are inherently noisy. Prior studies demonstrate that semantically aligning and enriching textual descriptions of these pairs can significantly enhance…
Medical Vision Generalist: Unifying Medical Imaging Tasks in Context
Sucheng Ren, Xiaoke Huang, Xianhang Li +5
This study presents Medical Vision Generalist (MVG), the first foundation model capable of handling various medical imaging tasks -- such as cross-modal synthesis, image segmentati…
Revisiting Adversarial Training at Scale
Zeyu Wang, Xianhang Li, Hongru Zhu +1
The machine learning community has witnessed a drastic change in the training pipeline, pivoted by those ''foundation models'' with unprecedented scales. However, the field of adve…
Rejuvenating image-GPT as Strong Visual Representation Learners
Sucheng Ren, Zeyu Wang, Hongru Zhu +3
This paper enhances image-GPT (iGPT), one of the pioneering works that introduce autoregressive pretraining to predict the next pixels for visual representation learning. Two simpl…
Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training
Yipeng Gao, Zeyu Wang, Wei-Shi Zheng +2
Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i.e., aligning point cloud representation to image and text embedding space individually.…
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
Peiran Xu, Zeyu Wang, Jieru Mei +4
Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data l…