1.4k citations · 4.2k across the 73 of their papers we have counts for
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Calibrating Uncertainty for Zero-Shot Adversarial CLIP
Wenjing Lu, Zerui Tao, Yuning Qiu +3
CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks. Prior adversarial fine-tuning work primarily matches predicted logits between cl…
Consist-Retinex: One-Step Noise-Emphasized Consistency Training Accelerates High-Quality Retinex Enhancement
Jian Xu, Wei Chen, Shigui Li +3
Retinex-based low-light image enhancement benefits from separating reflectance and illumination, yet recent generative approaches often rely on iterative sampling and are difficult…
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Binghua Li, Jie Mao, Zhe Sun +3
Automated diagnosis with artificial intelligence has emerged as a promising area in the realm of medical imaging, while the interpretability of the introduced deep neural networks…
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective
Wanqi Zhou, Shuanghao Bai, Danilo P. Mandic +2
Pretrained vision-language models (VLMs) like CLIP exhibit exceptional generalization across diverse downstream tasks. While recent studies reveal their vulnerability to adversaria…
Adversarial Guided Diffusion Models for Adversarial Purification
Guang Lin, Zerui Tao, Jianhai Zhang +2
Diffusion model (DM) based adversarial purification (AP) has proven to be a powerful defense method that can remove adversarial perturbations and generate a purified example withou…
Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization
Guang Lin, Chao Li, Jianhai Zhang +2
The deep neural networks are known to be vulnerable to well-designed adversarial attacks. The most successful defense technique based on adversarial training (AT) can achieve optim…
Multi-view Data Classification with a Label-driven Auto-weighted Strategy
Yuyuan Yu, Guoxu Zhou, Haonan Huang +2
Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-sup…
H-OWAN: Multi-distorted Image Restoration with Tensor 1x1 Convolution
Zihao Huang, Chao Li, Feng Duan +1
It is a challenging task to restore images from their variants with combined distortions. In the existing works, a promising strategy is to apply parallel "operations" to handle di…
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior
Tatsuya Yokota, Hidekata Hontani, Qibin Zhao +1
Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attracted attentions in computer vision and machine learning c…
Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completion
Jinshi Yu, Chao Li, Qibin Zhao +1
Tensor ring (TR) decomposition has been successfully used to obtain the state-of-the-art performance in the visual data completion problem. However, the existing TR-based completio…
Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
Wei He, Quanming Yao, Chao Li +2
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately, with more spectral bands for HSI, wh…
Smooth PARAFAC Decomposition for Tensor Completion
Tatsuya Yokota, Qibin Zhao, Andrzej Cichocki
In recent years, low-rank based tensor completion, which is a higher-order extension of matrix completion, has received considerable attention. However, the low-rank assumption is…
Bayesian Robust Tensor Factorization for Incomplete Multiway Data
Qibin Zhao, Guoxu Zhou, Liqing Zhang +2
We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CP-rank ten…