9 papers
Evidence-Grounded Forensic Reasoning for Detecting and Grounding Multi-Modal Media Manipulation
Yichun Yeh, Yiheng Li, Xiaobo Hu +2
Fake news increasingly relies on cross-modal image-text forgeries, making transparent and verifiable reasoning chains an urgent need for Detecting and Grounding Multi-Modal Media M…
Spectral Tail Auxiliary Learning for AI-Generated Image Detection
Xingyi Li, Jiahui Zhang, Yiheng Li +2
As generative image models evolve rapidly, the perceptual gap between generated and real images continues to narrow, making AI-generated image detection increasingly challenging. M…
Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection
Yiheng Li, Yang Yang, Wenhao Wang +4
As the misuse of AI-generated images grows, generalizable image detection techniques are urgently needed. Recent state-of-the-art (SOTA) methods adopt aligned training datasets to…
CTForensics: A Comprehensive Dataset and Method for AI-Generated CT Image Detection
Yiheng Li, Zichang Tan, Guoqing Xu +3
Recent advances in generative AI have made synthetic Computed Tomography (CT) images increasingly realistic, enabling promising applications in medical data augmentation while rais…
Fine-Grained Representation for Lane Topology Reasoning
Guoqing Xu, Yiheng Li, Yang Yang
Precise modeling of lane topology is essential for autonomous driving, as it directly impacts navigation and control decisions. Existing methods typically represent each lane with…
Towards Generalizable AI-Generated Image Detection via Image-Adaptive Prompt Learning
Yiheng Li, Zichang Tan, Guoqing Xu +3
In AI-generated image detection, current cutting-edge methods typically adapt pre-trained foundation models through partial-parameter fine-tuning. However, these approaches often s…
Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation
Yiheng Li, Yang Yang, Zichang Tan +4
To tackle the threat of fake news, the task of detecting and grounding multi-modal media manipulation DGM4 has received increasing attention. However, most state-of-the-art methods…
CoreNet: Conflict Resolution Network for Point-Pixel Misalignment and Sub-Task Suppression of 3D LiDAR-Camera Object Detection
Yiheng Li, Yang Yang, Zhen Lei
Fusing multi-modality inputs from different sensors is an effective way to improve the performance of 3D object detection. However, current methods overlook two important conflicts…
RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection
Yiheng Li, Yang Yang, Zhen Lei
In radar-camera 3D object detection, the radar point clouds are sparse and noisy, which causes difficulties in fusing camera and radar modalities. To solve this, we introduce a nov…