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Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting.
“Microsoft coco: Common objects in context,”
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick, · 2014
Earlier work this paper cites.
“Model-agnostic meta-learning for fast adaptation of deep networks,”
Chelsea Finn, Pieter Abbeel, and Sergey Levine, · 2017
Earlier work this paper cites.
“One-shot instance segmentation,”
Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge, and Alexander S Ecker, · 2018
Earlier work this paper cites.
“Class-agnostic counting,”
Erika Lu, Weidi Xie, and Andrew Zisserman, · 2018
Earlier work this paper cites.
“Few-shot object detection via feature reweighting,”
Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, and Trevor Darrell, · 2019
Earlier work this paper cites.
“Language models are few-shot learners,”
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al., · 2020
Earlier work this paper cites.
“Few-shot object detection with attention-rpn and multi-relation detector,”
Qi Fan, Wei Zhuo, Chi-Keung Tang, and Yu-Wing Tai, · 2020
Cited alongside, same era.
“Learning transferable visual models from natural language supervision,” 2021
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever, · 2021
Cited alongside, same era.
“Learning to count everything,”
Viresh Ranjan, Udbhav Sharma, Thu Nguyen, and Minh Hoai, · 2021
Cited alongside, same era.
“Class-agnostic few-shot object counting,”
Shuo-Diao Yang, Hung-Ting Su, Winston H Hsu, and Wen-Chin Chen, · 2021
Cited alongside, same era.
“Object counting: You only need to look at one,” 2021
Hui Lin, Xiaopeng Hong, and Yabin Wang, · 2021
Cited alongside, same era.
“Represent, compare, and learn: A similarity-aware framework for class-agnostic counting,”
“Gpt-4 technical report,” 2023
OpenAI, · 2023
Closest in time.
“Segment anything,” 2023
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick, · 2023
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“Grounding dino: Marrying dino with grounded pre-training for open-set object detection,”
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, and Lei Zhang, · 2023
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“Few-shot object counting with similarity-aware feature enhancement,”
Zhiyuan You, Kai Yang, Wenhan Luo, Xin Lu, Lei Cui, and Xinyi Le, · 2023
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“Accurate few-shot object counting with hough matching feature enhancement,”
Zhiquan He, Donghong Zheng, and Hengyou Wang, · 2023
Closest in time.
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Min Shi, Hao Lu, Chen Feng, Chengxin Liu, and Zhiguo Cao, · 2022
Cited alongside, same era.