Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations

16 February 2022

Papers citing "Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations"

7 / 57 papers shown

Title
Pruning Self-attentions into Convolutional Layers in Single Path Haoyu He Jianfei Cai Jing Liu Zizheng Pan Jing Zhang Dacheng Tao Bohan Zhuang ViT 34 40 0 23 Nov 2021
Intriguing Properties of Vision Transformers Muzammal Naseer Kanchana Ranasinghe Salman Khan Munawar Hayat F. Khan Ming-Hsuan Yang ViT 259 621 0 21 May 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 317 5,785 0 29 Apr 2021
Visformer: The Vision-friendly Transformer Zhengsu Chen Lingxi Xie Jianwei Niu Xuefeng Liu Longhui Wei Qi Tian ViT 120 209 0 26 Apr 2021
Transformer in Transformer Kai Han An Xiao Enhua Wu Jianyuan Guo Chunjing Xu Yunhe Wang ViT 287 1,524 0 27 Feb 2021
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao ViT 280 3,623 0 24 Feb 2021
Is Space-Time Attention All You Need for Video Understanding? Gedas Bertasius Heng Wang Lorenzo Torresani ViT 280 1,982 0 09 Feb 2021