Open AccessPosted Content
FcaNet: Frequency Channel Attention Networks
TLDR
Based on the frequency analysis, the authors mathematically proved that the conventional GAP is a special case of the feature decomposition in the frequency domain and proposed FCANet with novel multi-spectral channel attention.Abstract:
Attention mechanism, especially channel attention, has gained great success in the computer vision field. Many works focus on how to design efficient channel attention mechanisms while ignoring a fundamental problem, i.e., using global average pooling (GAP) as the unquestionable pre-processing method. In this work, we start from a different view and rethink channel attention using frequency analysis. Based on the frequency analysis, we mathematically prove that the conventional GAP is a special case of the feature decomposition in the frequency domain. With the proof, we naturally generalize the pre-processing of channel attention mechanism in the frequency domain and propose FcaNet with novel multi-spectral channel attention. The proposed method is simple but effective. We can change only one line of code in the calculation to implement our method within existing channel attention methods. Moreover, the proposed method achieves state-of-the-art results compared with other channel attention methods on image classification, object detection, and instance segmentation tasks. Our method could improve by 1.8% in terms of Top-1 accuracy on ImageNet compared with the baseline SENet-50, with the same number of parameters and the same computational cost. Our code and models are publicly available at this https URLread more
Citations
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Posted Content
Attention Mechanisms in Computer Vision: A Survey.
Meng-Hao Guo,Tian-Xing Xu,Jiangjiang Liu,Zheng-Ning Liu,Peng-Tao Jiang,Tai-Jiang Mu,Song-Hai Zhang,Ralph R. Martin,Ming-Ming Cheng,Shi-Min Hu +9 more
TL;DR: A comprehensive review of attention mechanisms in computer vision can be found in this article, which categorizes them according to approach, such as channel attention, spatial attention, temporal attention and branch attention.
Proceedings ArticleDOI
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
Jinjin Gu,Haoming Cai,Chao Dong,Jimmy Ren,Yu Qiao,Shuhang Gu,Radu Timofte,Manri Cheon,Sung-Jun Yoon,Byungyeon Kang,Junwoo Lee,Qing Zhang,Haiyang Guo,Bin Yi,Yuqing Hou,Hengliang Luo,Jingyu Guo,Zirui Wang,Hai Wang,Wenming Yang,Qingyan Bai,Shuwei Shi,Weihao Xia,Mingdeng Cao,Jiahao Wang,Yifan Chen,Yujiu Yang,Yang Li,Tao Zhang,Longtao Feng,Yiting Liao,Junlin Li,William Thong,Jose Costa Pereira,Ales Leonardis,Steven McDonagh,Kele Xu,Lehan Yang,Hengxing Cai,Pengfei Sun,Seyed Mehdi Ayyoubzadeh,Ali Royat,Sid Ahmed Fezza,Dounia Hammou,Wassim Hamidouche,Sewoong Ahn,Gwangjin Yoon,Koki Tsubota,Hiroaki Akutsu,Kiyoharu Aizawa +49 more
TL;DR: The NTIRE 2021 challenge on perceptual image quality assessment (IQA) as discussed by the authors was held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Posted Content
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
Jinjin Gu,Haoming Cai,Chao Dong,Jimmy Ren,Yu Qiao,Shuhang Gu,Radu Timofte,Manri Cheon,Sung-Jun Yoon,Byungyeon Kang,Junwoo Lee,Qing Zhang,Haiyang Guo,Bin Yi,Yuqing Hou,Hengliang Luo,Jingyu Guo,Zirui Wang,Hai Wang,Wenming Yang,Qingyan Bai,Shuwei Shi,Weihao Xia,Mingdeng Cao,Jiahao Wang,Yifan Chen,Yujiu Yang,Yang Li,Tao Zhang,Longtao Feng,Yiting Liao,Junlin Li,William Thong,Jose Costa Pereira,Ales Leonardis,Steven McDonagh,Kele Xu,Lehan Yang,Hengxing Cai,Pengfei Sun,Seyed Mehdi Ayyoubzadeh,Ali Royat,Sid Ahmed Fezza,Dounia Hammou,Wassim Hamidouche,Sewoong Ahn,Gwangjin Yoon,Koki Tsubota,Hiroaki Akutsu,Kiyoharu Aizawa +49 more
TL;DR: The NTIRE 2021 challenge on perceptual image quality assessment (IQA) as mentioned in this paper was held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Posted Content
Spatial-Angular Attention Network for Light Field Reconstruction
TL;DR: A spatial-angular attention network is proposed to perceive non-local correspondences in the light field, and reconstruct high angular resolution light field in an end-to-end manner with superior performance against sparsely-sampled light fields with Non-Lambertian effects.
Journal ArticleDOI
Spatial-Angular Attention Network for Light Field Reconstruction
TL;DR: Zhang et al. as mentioned in this paper propose a spatial-angular attention network to perceive non-local correspondences in the light field, and reconstruct high angular resolution light field in an end-to-end manner.
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