Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness

12 July 2024

Papers citing "Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness"

9 / 9 papers shown

Title
Spectral-Adaptive Modulation Networks for Visual Perception Guhnoo Yun J. Yoo Kijung Kim Jeongho Lee Paul Hongsuck Seo Dong Hwan Kim 32 0 0 31 Mar 2025
USP: Unified Self-Supervised Pretraining for Image Generation and Understanding Xiangxiang Chu Renda Li Yong Wang 60 0 0 08 Mar 2025
Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues X. Feng D. Zhang Shuyan Hu X. Li M. Wu Jie Zhang Xiaojing Chen K. Huang 40 0 0 27 Dec 2024
Patches Are All You Need? Asher Trockman J. Zico Kolter ViT 214 395 0 24 Jan 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,337 0 11 Nov 2021
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes David W. Romero Robert-Jan Bruintjes Jakub M. Tomczak Erik J. Bekkers Mark Hoogendoorn J. C. V. Gemert 74 81 0 15 Oct 2021
Intriguing Properties of Vision Transformers Muzammal Naseer Kanchana Ranasinghe Salman Khan Munawar Hayat F. Khan Ming-Hsuan Yang ViT 248 618 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 283 5,723 0 29 Apr 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 263 3,538 0 24 Feb 2021