19 citations · 37 across the 3 of their papers we have counts for
8 papers · 1 filter
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
Jiangtao Xie, Fei Long, Jiaming Lv +2
Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focus…
Temporal-attentive Covariance Pooling Networks for Video Recognition
Zilin Gao, Qilong Wang, Bingbing Zhang +2
For video recognition task, a global representation summarizing the whole contents of the video snippets plays an important role for the final performance. However, existing video…
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
Qilong Wang, Li Zhang, Banggu Wu +4
Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification tas…
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Qilong Wang, Banggu Wu, Pengfei Zhu +3
Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing me…
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
Qilong Wang, Jiangtao Xie, Wangmeng Zuo +2
Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potenti…
Global Second-order Pooling Convolutional Networks
Zilin Gao, Jiangtao Xie, Qilong Wang +1
Deep Convolutional Networks (ConvNets) are fundamental to, besides large-scale visual recognition, a lot of vision tasks. As the primary goal of the ConvNets is to characterize com…