3 citations · 5 across the 12 of their papers we have counts for
5 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…
Adding Thermal Awareness to Visual Systems in Real-Time via Distilled Diffusion Models
Yuchen Guo, Junli Gong, Wenjun Dong +2
Purely RGB-based vision models often fail to provide reliable cues in challenging scenarios such as nighttime and fog, leading to degraded performance and safety risks. Infrared im…
Semantic-guided Fine-tuning of Foundation Model for Long-tailed Visual Recognition
Yufei Peng, Yonggang Zhang, Yiu-ming Cheung
The variance in class-wise sample sizes within long-tailed scenarios often results in degraded performance in less frequent classes. Fortunately, foundation models, pre-trained on…
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…
GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
Xiufeng Huang, Ruiqi Li, Yiu-ming Cheung +3
3D Gaussian Splatting (3DGS) has become a crucial method for acquiring 3D assets. To protect the copyright of these assets, digital watermarking techniques can be applied to embed…