What Deep CNNs Benefit from Global Covariance Pooling: An Optimization
Perspective

What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective

25 March 2020

Li Zhang

Papers citing "What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective"

6 / 6 papers shown

Title
SoCov: Semi-Orthogonal Parametric Pooling of Covariance Matrix for Speaker Recognition Rongjin Li Weibin Zhang Dongpeng Chen Jintao Kang Xiaofen Xing 32 0 0 23 Apr 2025
Conditional Temporal Neural Processes with Covariance Loss Boseon Yoo Jiwoo Lee Janghoon Ju Seijun Chung Soyeon Kim Jaesik Choi 70 15 0 01 Apr 2025
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap? Nathan G. Drenkow Numair Sani I. Shpitser Mathias Unberath 19 74 0 01 Dec 2021
Fine-Grained Image Analysis with Deep Learning: A Survey Xiu-Shen Wei Yi-Zhe Song Oisin Mac Aodha Jianxin Wu Yuxin Peng Jinhui Tang Jian Yang Serge J. Belongie 71 279 0 11 Nov 2021
Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling? Yue Song N. Sebe Wei Wang 17 25 0 06 May 2021
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding Akira Fukui Dong Huk Park Daylen Yang Anna Rohrbach Trevor Darrell Marcus Rohrbach 152 1,465 0 06 Jun 2016