Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers

26 May 2024

Papers citing "Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers"

10 / 10 papers shown

Title
ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning Chau Pham Juan C. Caicedo Bryan A. Plummer 42 0 0 25 Mar 2025
Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning Wenyi Lian Joakim Lindblad Patrick Micke Natasa Sladoje 57 0 0 12 Mar 2025
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning Ruiqian Nai Zixin Wen Ji Li Yuanzhi Li Yang Gao 31 2 0 01 Mar 2024
Leveraging Relational Information for Learning Weakly Disentangled Representations Andrea Valenti D. Bacciu CoGe DRL 22 5 0 20 May 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 263 7,434 0 11 Nov 2021
RGB-D Saliency Detection via Cascaded Mutual Information Minimization Jing Zhang Deng-Ping Fan Yuchao Dai Xin Yu Yiran Zhong Nick Barnes Ling Shao 49 95 0 15 Sep 2021
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future Directions Andrea Burns Aaron Sarna Dilip Krishnan Aaron Maschinot CoGe DRL SSL 25 4 0 14 Aug 2021
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 173 313 0 07 Feb 2020
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 948 20,549 0 17 Apr 2017
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 273 10,214 0 16 Nov 2016