67 citations · 102 across the 6 of their papers we have counts for
6 papers
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
Chenglin Yang, Siyuan Qiao, Qihang Yu +5
This paper presents MOAT, a family of neural networks that build on top of MObile convolution (i.e., inverted residual blocks) and ATtention. Unlike the current works that stack se…
Lite Vision Transformer with Enhanced Self-Attention
Chenglin Yang, Yilin Wang, Jianming Zhang +4
Despite the impressive representation capacity of vision transformer models, current light-weight vision transformer models still suffer from inconsistent and incorrect dense predi…
Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms
Chenglin Yang, Siyuan Qiao, Adam Kortylewski +1
Self-Attention has become prevalent in computer vision models. Inspired by fully connected Conditional Random Fields (CRFs), we decompose self-attention into local and context term…
Meticulous Object Segmentation
Chenglin Yang, Yilin Wang, Jianming Zhang +3
Compared with common image segmentation tasks targeted at low-resolution images, higher resolution detailed image segmentation receives much less attention. In this paper, we propo…
Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks
Christian Cosgrove, Adam Kortylewski, Chenglin Yang +1
Patch-based adversarial attacks introduce a perceptible but localized change to the input that induces misclassification. While progress has been made in defending against impercep…
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
Chenglin Yang, Lingxi Xie, Siyuan Qiao +1
We focus on the problem of training a deep neural network in generations. The flowchart is that, in order to optimize the target network (student), another network (teacher) with t…