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The Segment Anything Model (SAM) stands as a foundational framework for image segmentation.
Machine learning for aerial image labeling
Volodymyr Mnih · 2013
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Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer
Juan Silva, Aymeric Histace, Olivier Romain, Xavier Dray, and Bertrand Granado · 2014
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Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
Jorge Bernal, F Javier Sánchez, Gloria Fernández-Esparrach, Debora Gil, Cristina Rodríguez, and Fernando Vilariño · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Automated polyp detection in colonoscopy videos using shape and context information
Nima Tajbakhsh, Suryakanth R Gurudu, and Jianming Liang · 2015
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Large-scale training of shadow detectors with noisily-annotated shadow examples
Tomás F Yago Vicente, Le Hou, Chen-Ping Yu, Minh Hoai, and Dimitris Samaras · 2016
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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A benchmark for endoluminal scene segmentation of colonoscopy images
David Vázquez, Jorge Bernal, F Javier Sánchez, Gloria Fernández-Esparrach, Antonio M López, Adriana Romero, Michal Drozdzal, Aaron Courville, et al · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al · 2018
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Animal camouflage analysis: Chameleon database
Przemysław Skurowski, Hassan Abdulameer, J Błaszczyk, Tomasz Depta, Adam Kornacki, and P Kozieł · 2018
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Detection of pests using color based image segmentation
Apurva Sriwastwa, Shikha Prakash, Swati Swarit, Khushboo Kumari, Sitanshu Sekhar Sahu, et al · 2018
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Road extraction from high-resolution remote sensing imagery using deep learning
Yongyang Xu, Zhong Xie, Yaxing Feng, and Zhanlong Chen · 2018
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Improved road connectivity by joint learning of orientation and segmentation
Anil Batra, Suriya Singh, Guan Pang, Saikat Basu, CV Jawahar, and Manohar Paluri · 2019
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Salient object detection: A survey
Ali Borji, Ming-Ming Cheng, Qibin Hou, Huaizu Jiang, and Jia Li · 2019
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Dual attention network for scene segmentation
Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Anabranch network for camouflaged object segmentation
Trung-Nghia Le, Tam V Nguyen, Zhongliang Nie, Minh-Triet Tran, and Akihiro Sugimoto · 2019
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Expectation-maximization attention networks for semantic segmentation
Xia Li, Zhisheng Zhong, Jianlong Wu, Yibo Yang, Zhouchen Lin, and Hong Liu · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Parameter-efficient transfer learning with diff pruning
Demi Guo, Alexander M Rush, and Yoon Kim · 2020
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Kvasir-seg: A segmented polyp dataset
Debesh Jha, Pia H Smedsrud, Michael A Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen, and Håvard D Johansen · 2020
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Segmenting transparent objects in the wild
Enze Xie, Wenjia Wang, Wenhai Wang, Mingyu Ding, Chunhua Shen, and Ping Luo · 2020
Cited alongside, same era.
Squeeze-and-attention networks for semantic segmentation
Zilong Zhong, Zhong Qiu Lin, Rene Bidart, Xiaodan Hu, Ibrahim Ben Daya, Zhifeng Li, Wei-Shi Zheng, Jonathan Li, and Alexander Wong · 2020
Cited alongside, same era.
Per-pixel classification is not all you need for semantic segmentation
Bowen Cheng, Alex Schwing, and Alexander Kirillov · 2021
Cited alongside, same era.
Concealed object detection
Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao · 2021
Cited alongside, same era.
Levit: a vision transformer in convnet’s clothing for faster inference
Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze · 2021
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Convolutional bypasses are better vision transformer adapters
Shibo Jie and Zhi-Hong Deng · 2022
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Scaling & shifting your features: A new baseline for efficient model tuning
Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang · 2022
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Expediting large-scale vision transformer for dense prediction without fine-tuning
Weicong Liang, Yuhui Yuan, Henghui Ding, Xiao Luo, Weihong Lin, Ding Jia, Zheng Zhang, Chao Zhang, and Han Hu · 2022
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Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Do vision transformers see like convolutional neural networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy · 2021
Cited alongside, same era.
Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
Cited alongside, same era.
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, and Ling Shao · 2021
Cited alongside, same era.
Namuk Park and Songkuk Kim · 2022
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Lst: Ladder side-tuning for parameter and memory efficient transfer learning
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
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Pvt v2: Improved baselines with pyramid vision transformer
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2022
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Mixture-of-experts with expert choice routing
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew M Dai, Quoc V Le, James Laudon, et al · 2022
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Ladder fine-tuning approach for sam integrating complementary network
Shurong Chai, Rahul Kumar Jain, Shiyu Teng, Jiaqing Liu, Yinhao Li, Tomoko Tateyama, and Yen-wei Chen · 2023
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Tianrun Chen, Lanyun Zhu, Chaotao Ding, Runlong Cao, Shangzhan Zhang, Yan Wang, Zejian Li, Lingyun Sun, Papa Mao, and Ying Zang · 2023
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Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, et al · 2023
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How to efficiently adapt large segmentation model (sam) to medical images
Xinrong Hu, Xiaowei Xu, and Yiyu Shi · 2023
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Segment anything is not always perfect: An investigation of sam on different real-world applications
Wei Ji, Jingjing Li, Qi Bi, Wenbo Li, and Li Cheng · 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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Gpt-4 technical report, 2023
OpenAI · 2023
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Leaf disease segmentation dataset, 2023
Sovit Ranjan Rath · 2023
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Autosam: Adapting sam to medical images by overloading the prompt encoder
Tal Shaharabany, Aviad Dahan, Raja Giryes, and Lior Wolf · 2023
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Can sam segment anything? when sam meets camouflaged object detection
Lv Tang, Haoke Xiao, and Bo Li · 2023
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Sam meets robotic surgery: An empirical study on generalization, robustness and adaptation
An Wang, Mobarakol Islam, Mengya Xu, Yang Zhang, and Hongliang Ren · 2023
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Medical sam adapter: Adapting segment anything model for medical image segmentation
Junde Wu, Rao Fu, Huihui Fang, Yuanpei Liu, Zhaowei Wang, Yanwu Xu, Yueming Jin, and Tal Arbel · 2023
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Side adapter network for open-vocabulary semantic segmentation
Mengde Xu, Zheng Zhang, Fangyun Wei, Han Hu, and Xiang Bai · 2023
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Customized segment anything model for medical image segmentation
Kaidong Zhang and Dong Liu · 2023
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Tao Zhou, Yizhe Zhang, Yi Zhou, Ye Wu, and Chen Gong · 2023
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