10 citations · 41 across the 8 of their papers we have counts for
8 papers
Contact Matrix: Enhancing Dance Motion Synthesis with Precise Interaction Modeling
Xuhai Chen, Zhi Cen, Huaijin Pi +3
Generating realistic reactive motions, in which one person reacts to the fixed motions of others, is challenging due to strict interaction constraints and a limited feasible soluti…
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection
Yuanpeng Tu, Boshen Zhang, Liang Liu +6
Industrial anomaly detection is generally addressed as an unsupervised task that aims at locating defects with only normal training samples. Recently, numerous 2D anomaly detection…
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection
Haoyang He, Jiangning Zhang, Hongxu Chen +6
Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have…
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
Jiangning Zhang, Xuhai Chen, Yabiao Wang +5
This work studies a challenging and practical issue known as multi-class unsupervised anomaly detection (MUAD). This problem requires only normal images for training while simultan…
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection
Jiangning Zhang, Haoyang He, Xuhai Chen +5
Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding capabilities, making it possible to handle certain tasks through the Visual Question Answering (VQA) p…
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection
Xuhai Chen, Jiangning Zhang, Guanzhong Tian +5
This paper considers zero-shot Anomaly Detection (AD), performing AD without reference images of the test objects. We propose a framework called CLIP-AD to leverage the zero-shot c…
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD
Xuhai Chen, Yue Han, Jiangning Zhang
In this technical report, we briefly introduce our solution for the Zero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. For industrial visual ins…
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Xuhai Chen, Jiangning Zhang, Chao Xu +3
Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-v…