388 citations · 795 across the 15 of their papers we have counts for
15 papers
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…
SoT: Delving Deeper into Classification Head for Transformer
Jiangtao Xie, Ruiren Zeng, Qilong Wang +2
Transformer models are not only successful in natural language processing (NLP) but also demonstrate high potential in computer vision (CV). Despite great advance, most of works on…
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…