3 citations · 9 across the 13 of their papers we have counts for
14 papers
Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection
Xuechao Zou, Yi Zhou, Kai Li +4
The growing realism and accessibility of manipulated and generated faces threaten the trustworthiness of digital media. To detect such forgeries, deepfake detectors based on vision…
Multi-Agent Forensic Reasoning for Generalizable Deepfake Video Detection
Xuechao Zou, Shun Zhang, Kai Li +6
The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety. Ho…
Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion
Yi Zhou, Xuechao Zou, Shun Zhang +7
Semi-supervised remote sensing (RS) image semantic segmentation offers a promising solution to alleviate the burden of exhaustive annotation, yet it fundamentally struggles with ps…
DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network
Zhiwei Huang, Yanwei Fu, Yi Zhou +3
LiDAR-camera extrinsic calibration is essential for multi-modal data fusion in robotic perception systems. However, existing approaches typically rely on handcrafted calibration ta…
Language as an Anchor: Preserving Relative Visual Geometry for Domain Incremental Learning
Shuyi Geng, Tao Zhou, Yi Zhou
A key challenge in Domain Incremental Learning (DIL) is to continually learn under shifting distributions while preserving knowledge from previous domains. Existing methods face a…
Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation
Zihao Chen, Yi Zhou, Xudong Jiang +4
Unpaired image-to-image translation has emerged as a crucial technique in medical imaging, enabling cross-modality synthesis, domain adaptation, and data augmentation without costl…
AutoIAD: Manager-Driven Multi-Agent Collaboration for Automated Industrial Anomaly Detection
Dongwei Ji, Bingzhang Hu, Yi Zhou
Industrial anomaly detection (IAD) is critical for manufacturing quality control, but conventionally requires significant manual effort for various application scenarios. This pape…
Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Ruiqi Wu, Yuang Yao, Tengfei Ma +11
Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities under reinforcement learning (RL) paradigm. However, most existing multimodal med…
Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities
Yuang Yao, Ruiqi Wu, Yi Zhou +1
Traditional fundus image analysis models focus on single-modal tasks, ignoring fundus modality complementarity, which limits their versatility. Recently, retinal foundation models…
MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining
Ruiqi Wu, Na Su, Chenran Zhang +10
Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievem…
A Post-Processing-Based Fair Federated Learning Framework
Yi Zhou, Naman Goel
Federated Learning (FL) allows collaborative model training among distributed parties without pooling local datasets at a central server. However, the distributed nature of FL pose…
Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey
Ao Fu, Yi Zhou, Tao Zhou +5
World models and video generation are pivotal technologies in the domain of autonomous driving, each playing a critical role in enhancing the robustness and reliability of autonomo…
Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge
Tianchen Deng, Yi Zhou, Wenhua Wu +9
This technical report presents the 1st winning model for UG2+, a task in CVPR 2024 UAV Tracking and Pose-Estimation Challenge. This challenge faces difficulties in drone detection,…
Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving
Lixing Xiao, Ruixiao Shi, Xiaoyang Tang +1
Previous works on object detection have achieved high accuracy in closed-set scenarios, but their performance in open-world scenarios is not satisfactory. One of the challenging op…