1 citations · 1 across the 6 of their papers we have counts for
6 papers
Risk Awareness Injection: Calibrating Vision-Language Models for Safety without Compromising Utility
Mengxuan Wang, Yuxin Chen, Gang Xu +3
Vision language models (VLMs) extend the reasoning capabilities of large language models (LLMs) to cross-modal settings, yet remain highly vulnerable to multimodal jailbreak attack…
EditInfinity: Image Editing with Binary-Quantized Generative Models
Jiahuan Wang, Yuxin Chen, Jun Yu +2
Adapting pretrained diffusion-based generative models for text-driven image editing with negligible tuning overhead has demonstrated remarkable potential. A classical adaptation pa…
Scaling Laws Revisited: Modeling the Role of Data Quality in Language Model Pretraining
Anirudh Subramanyam, Yuxin Chen, Robert L. Grossman
Scaling laws for language model training traditionally characterize how performance scales with model size and dataset volume. Prior work has explored architecture variants and dat…
Knowing or Guessing? Robust Medical Visual Question Answering via Joint Consistency and Contrastive Learning
Songtao Jiang, Yuxi Chen, Sibo Song +5
In high-stakes medical applications, consistent answering across diverse question phrasings is essential for reliable diagnosis. However, we reveal that current Medical Vision-Lang…
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
Yicheng Xu, Yuxin Chen, Jiahao Nie +3
Continual learning (CL) with Vision-Language Models (VLMs) has overcome the constraints of traditional CL, which only focuses on previously encountered classes. During the CL of VL…
Enhancing Instance-Level Image Classification with Set-Level Labels
Renyu Zhang, Aly A. Khan, Yuxin Chen +1
Instance-level image classification tasks have traditionally relied on single-instance labels to train models, e.g., few-shot learning and transfer learning. However, set-level coa…