4 citations · 7 across the 10 of their papers we have counts for
12 papers
DEFUSE: Generalizable Backdoor Defense for Self-Supervised Encoders with Generative Priors
Tuo Chen, Jie Gui, Minjing Dong +4
Self-supervised learning (SSL) encoders are vulnerable to backdoor attacks, posing threats to both visual SSL encoders and vision-language encoders. Existing defenses are typically…
Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking
Dongzhou Cheng, Ziang Li, Yixiao Zhou +6
Query-based Vision-Language-Action (VLA) models offer low-latency inference that is attractive for bimanual robotic manipulation, but we observe that they can still exhibit discont…
The Platonic Defense: Backdoor Defense for Self-Supervised Encoders in the Era of Large Scale Pre-training
Tuo Chen, Minjing Dong, Benlei Cui +2
Self-supervised learning (SSL) pretrained models have become a dominant paradigm for visual representation learning, but they are vulnerable to backdoor attacks. Existing defenses…
HDRFace: Rethinking Face Restoration with High-Dimensional Representation
Zirui Wang, Xianhui Lin, Yi Dong +7
Face restoration under complex degradations still remains an ill-posed inverse problem due to severe information loss. Although diffusion models benefit from strong generative prio…
Diversifying Counterattacks: Orthogonal Exploration for Robust CLIP Inference
Chengze Jiang, Minjing Dong, Xinli Shi +1
Vision-language pre-training models (VLPs) demonstrate strong multimodal understanding and zero-shot generalization, yet remain vulnerable to adversarial examples, raising concerns…
Revisiting Adversarial Training under Hyperspectral Image
Weihua Zhang, Chengze Jiang, Minjing Dong +5
Recent studies have shown that deep learning-based hyperspectral image (HSI) classification models are highly vulnerable to adversarial attacks, posing significant security risks.…
Backdooring Self-Supervised Contrastive Learning by Noisy Alignment
Tuo Chen, Jie Gui, Minjing Dong +3
Self-supervised contrastive learning (CL) effectively learns transferable representations from unlabeled data containing images or image-text pairs but suffers vulnerability to dat…
A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions
Shuxian Zhao, Jie Gui, Minjing Dong +5
The small sample imbalance (S&I) problem is a major challenge in machine learning and data analysis. It is characterized by a small number of samples and an imbalanced class distri…
Survey of Adversarial Robustness in Multimodal Large Language Models
Chengze Jiang, Zhuangzhuang Wang, Minjing Dong +1
Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance in artificial intelligence by facilitating integrated understanding across diverse modalities, in…
Improving Fast Adversarial Training via Self-Knowledge Guidance
Chengze Jiang, Junkai Wang, Minjing Dong +5
Adversarial training has achieved remarkable advancements in defending against adversarial attacks. Among them, fast adversarial training (FAT) is gaining attention for its ability…
Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
Jie Gui, Chengze Jiang, Minjing Dong +4
While adversarial training is an effective defense method against adversarial attacks, it notably increases the training cost. To this end, fast adversarial training (FAT) is prese…
Exploring the Coordination of Frequency and Attention in Masked Image Modeling
Jie Gui, Tuo Chen, Minjing Dong +4
Recently, masked image modeling (MIM), which learns visual representations by reconstructing the masked patches of an image, has dominated self-supervised learning in computer visi…