5 papers · 1 filter
Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models
Xiao Liu, Jiaxiang Liu, Boci Peng +6
Vision Language Models adapt well to downstream tasks but are highly vulnerable to adversarial perturbations that disrupt cross-modal semantic alignment. Existing defenses are larg…
MindAdapter: Few-Shot Parameter-Efficient Residual Calibration of Cross-Subject Brain-to-Visual Decoding Models
Jiaxiang Liu, Jiawei Du, Xupeng Chen +4
Cross-subject brain-to-visual decoding remains a core challenge in brain-computer interfaces due to severe inter-individual variability that induces systematic subject-specific fun…
MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis
Feng Guo, Jiaxiang Liu, Yang Li +2
Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging manifestations, yet existing publ…
Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models
Jiaxiang Liu, Jiawei Du, Xiao Liu +2
Pre-trained vision-language models (VLMs) such as CLIP have demonstrated strong zero-shot capabilities across diverse domains, yet remain highly vulnerable to adversarial perturbat…
Modest-Align: Data-Efficient Alignment for Vision-Language Models
Jiaxiang Liu, Yuan Wang, Jiawei Du +3
Cross-modal alignment aims to map heterogeneous modalities into a shared latent space, as exemplified by models like CLIP, which benefit from large-scale image-text pretraining for…