research
∙
10/04/2022
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
This paper presents MOAT, a family of neural networks that build on top ...
share
research
∙
12/20/2021
Lite Vision Transformer with Enhanced Self-Attention
Despite the impressive representation capacity of vision transformer mod...
research
∙
07/12/2021
Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms
Self-Attention has become prevalent in computer vision models. Inspired ...
research
∙
12/13/2020
Meticulous Object Segmentation
Compared with common image segmentation tasks targeted at low-resolution...
research
∙
12/01/2020
Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks
Patch-based adversarial attacks introduce a perceptible but localized ch...
research
∙
04/12/2020
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
Patch-based attacks introduce a perceptible but localized change to the ...
research
∙
12/01/2018
Snapshot Distillation: Teacher-Student Optimization in One Generation
Optimizing a deep neural network is a fundamental task in computer visio...
research
∙
05/15/2018
