318 citations · 678 across the 12 of their papers we have counts for
12 papers
RAID: Retrieval-Augmented Anomaly Detection
Mingxiu Cai, Zhe Zhang, Gaochang Wu +2
Unsupervised Anomaly Detection (UAD) aims to identify abnormal regions by establishing correspondences between test images and normal templates. Existing methods primarily rely on…
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering
Zhe Zhang, Mingxiu Cai, Hanxiao Wang +3
Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an input image with respect to normal samples. Either by reconstructing normal counterparts (reconstructi…
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark
Gaochang Wu, Yapeng Zhang, Lan Deng +2
Anomaly detection in complex industrial processes plays a pivotal role in ensuring efficient, stable, and secure operation. Existing anomaly detection methods primarily focus on an…
Unified Domain Adaptive Semantic Segmentation
Zhe Zhang, Gaochang Wu, Jing Zhang +3
Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled target domain. The majority of existing UD…
Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering
Gaochang Wu, Yuemei Zhou, Yebin Liu +2
In this paper, we present a Geometry-aware Neural Interpolation (Geo-NI) framework for light field rendering. Previous learning-based approaches either rely on the capability of ne…
Disentangling Light Fields for Super-Resolution and Disparity Estimation
Yingqian Wang, Longguang Wang, Gaochang Wu +4
Light field (LF) cameras record both intensity and directions of light rays, and encode 3D scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have been p…
LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation
Ruizhi Shao, Gaochang Wu, Yuemei Zhou +2
Cross-resolution image alignment is a key problem in multiscale gigapixel photography, which requires to estimate homography matrix using images with large resolution gap. Existing…
Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network
Gaochang Wu, Yebin Liu, Lu Fang +1
The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and the non-Lambertian effect. Typical approaches either address the large disparity c…
Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications
Gaochang Wu, Yebin Liu, Lu Fang +2
In this paper, a novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We indicate that the reconstructio…
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images
Yuemei Zhou, Gaochang Wu, Ying Fu +2
Various combinations of cameras enrich computational photography, among which reference-based superresolution (RefSR) plays a critical role in multiscale imaging systems. However,…
Spatial-Angular Attention Network for Light Field Reconstruction
Gaochang Wu, Yingqian Wang, Yebin Liu +2
Typical learning-based light field reconstruction methods demand in constructing a large receptive field by deepening the network to capture correspondences between input views. In…
LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction
Gaochang Wu, Yebin Liu, Lu Fang +1
For dense sampled light field (LF) reconstruction problem, existing approaches focus on a depth-free framework to achieve non-Lambertian performance. However, they trap in the trad…