28 citations · 92 across the 52 of their papers we have counts for
9 papers · 1 filter
Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection
Jun Nie, Yonggang Zhang, Tongliang Liu +3
Robust detection of generated images is critical to counter the misuse of generative models. Existing methods primarily depend on learning from human-annotated training datasets, l…
Rethinking Model Selection in VLM Through the Lens of Gromov-Wasserstein Distance
Muyang Li, Yucheng Liu, Jianbo Ma +3
Vision-Language Models (VLMs) have enhanced traditional LLMs with visual capabilities through the integration of vision encoders. While recent works have explored various combinati…
Epistemic Uncertainty for Generated Image Detection
Jun Nie, Yonggang Zhang, Tongliang Liu +3
We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models. Our key…
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning
Hongduan Tian, Feng Liu, Zhanke Zhou +3
In cross-domain few-shot classification (CFC), recent works mainly focus on adapting a simple transformation head on top of a frozen pre-trained backbone with few labeled data to p…
Negative Label Guided OOD Detection with Pretrained Vision-Language Models
Xue Jiang, Feng Liu, Zhen Fang +4
Out-of-distribution (OOD) detection aims at identifying samples from unknown classes, playing a crucial role in trustworthy models against errors on unexpected inputs. Extensive re…
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation
PengFei Zheng, Yonggang Zhang, Zhen Fang +3
Image interpolation based on diffusion models is promising in creating fresh and interesting images. Advanced interpolation methods mainly focus on spherical linear interpolation,…
Few-Shot Adversarial Prompt Learning on Vision-Language Models
Yiwei Zhou, Xiaobo Xia, Zhiwei Lin +2
The vulnerability of deep neural networks to imperceptible adversarial perturbations has attracted widespread attention. Inspired by the success of vision-language foundation model…
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
Zhuo Huang, Chang Liu, Yinpeng Dong +3
Although vision models such as Contrastive Language-Image Pre-Training (CLIP) show impressive generalization performance, their zero-shot robustness is still limited under Out-of-D…
Gradient constrained sharpness-aware prompt learning for vision-language models
Liangchen Liu, Nannan Wang, Dawei Zhou +4
This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM), i.e., improving the performance on unseen classes while maintaining…