R-Cut: Enhancing Explainability in Vision Transformers with Relationship
Weighted Out and Cut

R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut

18 July 2023

Ming Ding

Papers citing "R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut"

6 / 6 papers shown

Title
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 351 12,003 0 04 Mar 2022
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Junnan Li Dongxu Li Caiming Xiong Guosheng Lin MLLM BDL VLM CLIP 392 4,154 0 28 Jan 2022
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 359 5,811 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 316 3,625 0 24 Feb 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 950 20,572 0 17 Apr 2017
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,217 0 01 Sep 2014