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In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model.
Very deep convolutional networks for large-scale image recognition
Yoshua Bengio and Yann LeCun · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep residual learning for image recognition
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Matching networks for one shot learning
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One-shot learning for semantic segmentation
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots · 2017
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Attention is all you need
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Decoupled weight decay regularization
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Feature weighting and boosting for few-shot segmentation
Khoi Nguyen and Sinisa Todorovic · 2019
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Panet: Few-shot image semantic segmentation with prototype alignment
Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng · 2019
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Free-form image inpainting with gated convolution
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2019
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Prior guided feature enrichment network for few-shot segmentation
Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia · 2020
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Prototype mixture models for few-shot semantic segmentation
Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye · 2020
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Few-shot segmentation without meta-learning: A good transductive inference is all you need?
Malik Boudiaf, Hoel Kervadec, Ziko Imtiaz Masud, Pablo Piantanida, Ismail Ben Ayed, and Jose Dolz · 2021
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Simpler is better: Few-shot semantic segmentation with classifier weight transformer
Zhihe Lu, Sen He, Xiatian Zhu, Li Zhang, Yi-Zhe Song, and Tao Xiang · 2021
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Hypercorrelation squeeze for few-shot segmentation
Juhong Min, Dahyun Kang, and Minsu Cho · 2021
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Ssa: Semantic structure aware inference for weakly pixel-wise dense predictions without cost
Yanpeng Sun and Zechao Li · 2021
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Few-shot segmentation via cycle-consistent transformer
Gengwei Zhang, Guoliang Kang, Yi Yang, and Yunchao Wei · 2021
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Visual prompting via image inpainting
Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, and Alexei Efros · 2022
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Self-support few-shot semantic segmentation
Qi Fan, Wenjie Pei, Yu-Wing Tai, and Chi-Keung Tang · 2022
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Cost aggregation with 4d convolutional swin transformer for few-shot segmentation
Sunghwan Hong, Seokju Cho, Jisu Nam, Stephen Lin, and Seungryong Kim · 2022
Samaug: Point prompt augmentation for segment anything model
Haixing Dai, Chong Ma, Zhengliang Liu, Yiwei Li, Peng Shu, Xiaozheng Wei, Lin Zhao, Zihao Wu, Dajiang Zhu, Wei Liu, et al · 2023
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Deep learning universal crater detection using segment anything model (sam)
Iraklis Giannakis, Anshuman Bhardwaj, Lydia Sam, and Georgios Leontidis · 2023
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Segment anything in high quality
Lei Ke, Mingqiao Ye, Martin Danelljan, Yifan Liu, Yu-Wing Tai, Chi-Keung Tang, and Fisher Yu · 2023
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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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Dense gaussian processes for few-shot segmentation
Joakim Johnander, Johan Edstedt, Michael Felsberg, Fahad Shahbaz Khan, and Martin Danelljan · 2022
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Learning what not to segment: A new perspective on few-shot segmentation
Chunbo Lang, Gong Cheng, Binfei Tu, and Junwei Han · 2022
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Hm: Hybrid masking for few-shot segmentation
Seonghyeon Moon, Samuel S Sohn, Honglu Zhou, Sejong Yoon, Vladimir Pavlovic, Muhammad Haris Khan, and Mubbasir Kapadia · 2022
Cited alongside, same era.
Dense cross-query-and-support attention weighted mask aggregation for few-shot segmentation
Xinyu Shi, Dong Wei, Yu Zhang, Donghuan Lu, Munan Ning, Jiashun Chen, Kai Ma, and Yefeng Zheng · 2022
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Singular value fine-tuning: Few-shot segmentation requires few-parameters fine-tuning
Yanpeng Sun, Qiang Chen, Xiangyu He, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jian Cheng, Zechao Li, and Jingdong Wang · 2022
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Feature-proxy transformer for few-shot segmentation
Jian-Wei Zhang, Yifan Sun, Yi Yang, and Wei Chen · 2022
Cited alongside, same era.
Yang Liu, Muzhi Zhu, Hengtao Li, Hao Chen, Xinlong Wang, and Chunhua Shen · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Hierarchical dense correlation distillation for few-shot segmentation
Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chengyao Wang, Shu Liu, Jingyong Su, and Jiaya Jia · 2023
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Token contrast for weakly-supervised semantic segmentation
Lixiang Ru, Heliang Zheng, Yibing Zhan, and Bo Du · 2023
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Exploring effective factors for improving visual in-context learning
Yanpeng Sun, Qiang Chen, Jian Wang, Jingdong Wang, and Zechao Li · 2023
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Edit everything: A text-guided generative system for images editing
Defeng Xie, Ruichen Wang, Jian Ma, Chen Chen, Haonan Lu, Dong Yang, Fobo Shi, and Xiaodong Lin · 2023
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Track anything: Segment anything meets videos
Jinyu Yang, Mingqi Gao, Zhe Li, Shang Gao, Fangjing Wang, and Feng Zheng · 2023
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Matte anything: Interactive natural image matting with segment anything models
Jingfeng Yao, Xinggang Wang, Lang Ye, and Wenyu Liu · 2023
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Inpaint anything: Segment anything meets image inpainting
Tao Yu, Runseng Feng, Ruoyu Feng, Jinming Liu, Xin Jin, Wenjun Zeng, and Zhibo Chen · 2023
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Segment everything everywhere all at once
Xueyan Zou, Jianwei Yang, Hao Zhang, Feng Li, Linjie Li, Jianfeng Gao, and Yong Jae Lee · 2023
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