34 citations · 37 across the 12 of their papers we have counts for
12 papers
FeatDistill: A Feature Distillation Enhanced Multi-Expert Ensemble Framework for Robust AI-generated Image Detection
Zhilin Tu, Kemou Li, Fengpeng Li +3
The rapid iteration and widespread dissemination of deepfake technology have posed severe challenges to information security, making robust and generalizable detection of AI-genera…
Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models
Rui Hu, Delai Qiu, Shuyu Wei +4
Omnimodal Large Language Models (OLLMs) have shown significant progress in integrating vision and text, but still struggle with integrating vision and audio, often exhibiting subop…
Debiasing Vison-Language Models with Text-Only Training
Yunfan Yang, Chaoquan Jiang, Zhiyu Lin +3
Pre-trained vision-language models (VLMs), such as CLIP, have exhibited remarkable performance across various downstream tasks by aligning text and images in a unified embedding sp…
Adversarial Prompt Tuning for Vision-Language Models
Jiaming Zhang, Xingjun Ma, Xin Wang +4
With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual…
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks
Jiaming Zhang, Lingyu Qiu, Qi Yi +4
The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks poses a significant challenge to their deployment in safety-critical applications. While extensive research…
Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples
Jiaming Zhang, Xingjun Ma, Qi Yi +4
There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noi…