336 citations · 853 across the 11 of their papers we have counts for
13 papers
Exploiting Features with Split-and-Share Module
Jaemin Lee, Minseok Seo, Jongchan Park +1
Deep convolutional neural networks (CNNs) have shown state-of-the-art performances in various computer vision tasks. Advances on CNN architectures have focused mainly on designing…
Learning Visual Context by Comparison
Minchul Kim, Jongchan Park, Seil Na +2
Finding diseases from an X-ray image is an important yet highly challenging task. Current methods for solving this task exploit various characteristics of the chest X-ray image, bu…
Sequential Feature Filtering Classifier
Minseok Seo, Jaemin Lee, Jongchan Park +1
We propose Sequential Feature Filtering Classifier (FFC), a simple but effective classifier for convolutional neural networks (CNNs). With sequential LayerNorm and ReLU, FFC zeroes…
Reducing Domain Gap by Reducing Style Bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park +2
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies sug…
CBAM: Convolutional Block Attention Module
Sanghyun Woo, Jongchan Park, Joon-Young Lee +1
We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, o…
BAM: Bottleneck Attention Module
Jongchan Park, Sanghyun Woo, Joon-Young Lee +1
Recent advances in deep neural networks have been developed via architecture search for stronger representational power. In this work, we focus on the effect of attention in genera…