activity
20182022
most citedBoosting Out-of-distribution Detection with Typical Features

17 citations · 23 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV2022★ 17 cited

Boosting Out-of-distribution Detection with Typical Features

Yao Zhu, YueFeng Chen, Chuanlong Xie +6

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD det…

cs.CV2020

Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization

Tingyu Wang, Zhedong Zheng, Chenggang Yan +4

Cross-view geo-localization is to spot images of the same geographic target from different platforms, e.g., drone-view cameras and satellites. It is challenging in the large visual…

cs.CV2020★ 3 cited

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Shanxin Yuan, Radu Timofte, Ales Leonardis +43

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demo…

cs.CV2020

Image Demoireing with Learnable Bandpass Filters

Bolun Zheng, Shanxin Yuan, Gregory Slabaugh +1

Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural…

eess.IV2019★ 3 cited

AIM 2019 Challenge on Image Demoireing: Methods and Results

Shanxin Yuan, Radu Timofte, Gregory Slabaugh +25

This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper desc…

cs.CV2018

S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction

Bolun Zheng, Rui Sun, Xiang Tian +1

Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach e…