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The Segment Anything Model (SAM) is a promptable segmentation model recently introduced by Meta AI that has demonstrated its prowess across various fields beyond just image segmentation.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
How to evaluate foreground maps?
Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
Learning efficient object detection models with knowledge distillation
Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, and Manmohan Chandraker · 2017
Cited alongside, same era.
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017
Cited alongside, same era.
Self-supervised knowledge distillation using singular value decomposition
Seung Hyun Lee, Dae Ha Kim, and Byung Cheol Song · 2018
Cited alongside, same era.
Conditional teacher-student learning
Zhong Meng, Jinyu Li, Yong Zhao, and Yifan Gong · 2019
Cited alongside, same era.
Fast human pose estimation
Feng Zhang, Xiatian Zhu, and Mao Ye · 2019
Later among the works it cites.
Segmenting objects in day and night: Edge-conditioned cnn for thermal image semantic segmentation
Chenglong Li, Wei Xia, Yan Yan, Bin Luo, and Jin Tang · 2020
Later among the works it cites.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Later among the works it cites.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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