activity
20242026
collaborators

10 papers

cs.CV2026

PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation

Yanjie Pan, Qingdong He, Zhengkai Jiang +9

Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing ControlNet-like methods struggle…

cs.CV2026

Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection

Wenbing Zhu, Jianing Liang, Linjie Cheng +7

Industrial Anomaly Detection (IAD) is critical for quality control, but existing methods struggle with subtle, geometric defects. Standard 2D (RGB) images are sensitive to texture…

cs.CV2026

Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era

Wenbing Zhu, Chengjie Wang, Bin-Bin Gao +12

Industrial Anomaly Detection (IAD) is a cornerstone for ensuring operational safety, maintaining product quality, and optimizing manufacturing efficiency. However, the advancement…

cs.CV2025

Once Is Enough: Lightweight DiT-Based Video Virtual Try-On via One-Time Garment Appearance Injection

Yanjie Pan, Qingdong He, Lidong Wang +2

Video virtual try-on aims to replace the clothing of a person in a video with a target garment. Current dual-branch architectures have achieved significant success in diffusion mod…

cs.CV2025

UniCombine: Unified Multi-Conditional Combination with Diffusion Transformer

Haoxuan Wang, Jinlong Peng, Qingdong He +9

With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing. Although existing methods can g…

cs.CV2025

Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection

Ziqing Zhou, Yurui Pan, Lidong Wang +4

Prototype-based reconstruction methods for unsupervised anomaly detection utilize a limited set of learnable prototypes which only aggregates insufficient normal information, resul…