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The advent of large models, also known as foundation models, has significantly transformed the AI research landscape, with models like Segment Anything (SAM) achieving notable success in diverse image segmentation scenarios.
A computational approach to edge detection
John Canny · 1986
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Detecting moving objects, ghosts, and shadows in video streams
Rita Cucchiara, Costantino Grana, Massimo Piccardi, and Andrea Prati · 2003
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Physical models for moving shadow and object detection in video
Sohail Nadimi and Bir Bhanu · 2004
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Performance of decamouflaging through exploratory image analysis
P Sengottuvelan, Amitabh Wahi, and A Shanmugam · 2008
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Learning to recognize shadows in monochromatic natural images
Jiejie Zhu, Kegan GG Samuel, Syed Z Masood, and Marshall F Tappen · 2010
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Detection of the mobile object with camouflage color under dynamic background based on optical flow
Jianqin Yin Yanbin Han Wendi Hou and Jinping Li · 2011
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What characterizes a shadow boundary under the sun and sky?
Xiang Huang, Gang Hua, Jack Tumblin, and Lance Williams · 2011
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Rendering synthetic objects into legacy photographs
Kevin Karsch, Varsha Hedau, David Forsyth, and Derek Hoiem · 2011
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Medico multimedia task at mediaeval 2020: Automatic polyp segmentation
Debesh Jha, Steven A Hicks, Krister Emanuelsen, Håvard Johansen, Dag Johansen, Thomas de Lange, Michael A Riegler, and Pål Halvorsen · 2012
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Estimating the natural illumination conditions from a single outdoor image
Jean-François Lalonde, Alexei A Efros, and Srinivasa G Narasimhan · 2012
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Camouflage texture evaluation using saliency map
Xue Feng, Cui Guoying, and Song Wei · 2013
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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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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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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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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 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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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Challenges on large scale surveillance video analysis
Weitao Feng, Deyi Ji, Yiru Wang, Shuorong Chang, Hansheng Ren, and Weihao Gan · 2018
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Direction-aware spatial context features for shadow detection
Xiaowei Hu, Lei Zhu, Chi-Wing Fu, Jing Qin, and Pheng-Ann Heng · 2018
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A+ d net: Training a shadow detector with adversarial shadow attenuation
Hieu Le, Tomas F Yago Vicente, Vu Nguyen, Minh Hoai, and Dimitris Samaras · 2018
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Quantifying camouflage and conspicuousness using visual salience
Thomas W Pike · 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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Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal
Jifeng Wang, Xiang Li, and Jian Yang · 2018
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Unet++: A nested u-net architecture for medical image segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang · 2018
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Selective feature aggregation network with area-boundary constraints for polyp segmentation
Yuqi Fang, Cheng Chen, Yixuan Yuan, and Kai-yu Tong · 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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End to end multi-scale convolutional neural network for crowd counting
Deyi Ji, Hongtao Lu, and Tongzhen Zhang · 2019
Cited alongside, same era.
Anabranch network for camouflaged object segmentation
Trung-Nghia Le, Tam V Nguyen, Zhongliang Nie, Minh-Triet Tran, and Akihiro Sugimoto · 2019
Cited alongside, same era.
Psi-net: Shape and boundary aware joint multi-task deep network for medical image segmentation, 2019
Vision transformer adapter for dense predictions
Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao · 2022
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Masked-attention mask transformer for universal image segmentation
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar · 2022
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Panoptic nerf: 3d-to-2d label transfer for panoptic urban scene segmentation
Xiao Fu, Shangzhan Zhang, Tianrun Chen, Yichong Lu, Lanyun Zhu, Xiaowei Zhou, Andreas Geiger, and Yiyi Liao · 2022
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Non-equivalent images and pixels: Confidence-aware resampling with meta-learning mixup for polyp segmentation
Xiaoqing Guo, Zhen Chen, Jun Liu, and Yixuan Yuan · 2022
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Structural and statistical texture knowledge distillation for semantic segmentation
Deyi Ji, Haoran Wang, Mingyuan Tao, Jianqiang Huang, Xian-Sheng Hua, and Hongtao Lu · 2022
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Balamurali Murugesan, Kaushik Sarveswaran, Sharath M Shankaranarayana, Keerthi Ram, and Mohanasankar Sivaprakasam · 2019
Cited alongside, same era.
Bert and pals: Projected attention layers for efficient adaptation in multi-task learning
Asa Cooper Stickland and Iain Murray · 2019
Cited alongside, same era.
Distraction-aware shadow detection
Quanlong Zheng, Xiaotian Qiao, Ying Cao, and Rynson WH Lau · 2019
Cited alongside, same era.
Asymmetric non-local neural networks for semantic segmentation
Zhen Zhu, Mengde Xu, Song Bai, Tengteng Huang, and Xiang Bai · 2019
Cited alongside, same era.
Towards ghost-free shadow removal via dual hierarchical aggregation network and shadow matting gan
Xiaodong Cun, Chi-Man Pun, and Cheng Shi · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Class-wise dynamic graph convolution for semantic segmentation
Hanzhe Hu, Deyi Ji, Weihao Gan, Shuai Bai, Wei Wu, and Junjie Yan · 2020
Cited alongside, same era.
Later among the works it cites.
Exploring plain vision transformer backbones for object detection
Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He · 2022
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Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging, 2023
Ruining Deng, Can Cui, Quan Liu, Tianyuan Yao, Lucas W. Remedios, Shunxing Bao, Bennett A. Landman, Lee E. Wheless, Lori A. Coburn, Keith T. Wilson, Yaohong Wang, Shilin Zhao, Agnes B. Fogo, Haichun Yang, Yucheng Tang, and Yuankai Huo · 2023
Later among the works it cites.
Segment anything in high quality, 2023
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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Polyp-sam: Transfer sam for polyp segmentation, 2023
Yuheng Li, Mingzhe Hu, and Xiaofeng Yang · 2023
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Explicit visual prompting for low-level structure segmentations
Weihuang Liu, Xi Shen, Chi-Man Pun, and Xiaodong Cun · 2023
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Segment anything model for medical image analysis: An experimental study
Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, and Yixin Zhang · 2023
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Segment anything, from space?, 2023
Simiao Ren, Francesco Luzi, Saad Lahrichi, Kaleb Kassaw, Leslie M. Collins, Kyle Bradbury, and Jordan M. Malof · 2023
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Sam.md: Zero-shot medical image segmentation capabilities of the segment anything model, 2023
Saikat Roy, Tassilo Wald, Gregor Koehler, Maximilian R. Rokuss, Nico Disch, Julius Holzschuh, David Zimmerer, and Klaus H. Maier-Hein · 2023
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Can sam segment anything? when sam meets camouflaged object detection, 2023
Lv Tang, Haoke Xiao, and Bo Li · 2023
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Fvp: Fourier visual prompting for source-free unsupervised domain adaptation of medical image segmentation
Yan Wang, Jian Cheng, Yixin Chen, Shuai Shao, Lanyun Zhu, Zhenzhou Wu, Tao Liu, and Haogang Zhu · 2023
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Medical sam adapter: Adapting segment anything model for medical image segmentation, 2023
Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu, Min Xu, Yanwu Xu, and Yueming Jin · 2023
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Efficientsam: Leveraged masked image pretraining for efficient segment anything, 2023
Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, and Vikas Chandra · 2023
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Faster segment anything: Towards lightweight sam for mobile applications, 2023
Chaoning Zhang, Dongshen Han, Yu Qiao, Jung Uk Kim, Sung-Ho Bae, Seungkyu Lee, and Choong Seon Hong · 2023
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Fast segment anything, 2023
Xu Zhao, Wenchao Ding, Yongqi An, Yinglong Du, Tao Yu, Min Li, Ming Tang, and Jinqiao Wang · 2023
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Can sam segment polyps?, 2023
Tao Zhou, Yizhe Zhang, Yi Zhou, Ye Wu, and Chen Gong · 2023
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Changenet: Multi-temporal asymmetric change detection dataset
Deyi Ji, Siqi Gao, Mingyuan Tao, Hongtao Lu, and Feng Zhao · 2024
Closest in time.
Sam 2: Segment anything in images and videos, 2024
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Feichtenhofer · 2024
Closest in time.
Moving object segmentation: All you need is sam (and flow), 2024
Junyu Xie, Charig Yang, Weidi Xie, and Andrew Zisserman · 2024
Closest in time.
Resmatch: Referring expression segmentation in a semi-supervised manner
Ying Zang, Chenglong Fu, Runlong Cao, Didi Zhu, Min Zhang, Wenjun Hu, Lanyun Zhu, and Tianrun Chen · 2024
Closest in time.