167 citations · 282 across the 7 of their papers we have counts for
11 papers
Reduce Information Loss in Transformers for Pluralistic Image Inpainting
Qiankun Liu, Zhentao Tan, Dongdong Chen +6
Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer f…
MiniViT: Compressing Vision Transformers with Weight Multiplexing
Jinnian Zhang, Houwen Peng, Kan Wu +4
Vision Transformer (ViT) models have recently drawn much attention in computer vision due to their high model capability. However, ViT models suffer from huge number of parameters,…
MicroNet: Improving Image Recognition with Extremely Low FLOPs
Yunsheng Li, Yinpeng Chen, Xiyang Dai +6
This paper aims at addressing the problem of substantial performance degradation at extremely low computational cost (e.g. 5M FLOPs on ImageNet classification). We found that two f…
Dynamic Head: Unifying Object Detection Heads with Attentions
Xiyang Dai, Yinpeng Chen, Bin Xiao +4
The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the perfo…
CvT: Introducing Convolutions to Vision Transformers
Haiping Wu, Bin Xiao, Noel Codella +4
We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convo…
Revisiting Dynamic Convolution via Matrix Decomposition
Yunsheng Li, Yinpeng Chen, Xiyang Dai +7
Recent research in dynamic convolution shows substantial performance boost for efficient CNNs, due to the adaptive aggregation of K static convolution kernels. It has two limitatio…