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The Segment Anything Model (SAM), introduced to the computer vision community by Meta in April 2023, is a groundbreaking tool that allows automated segmentation of objects in images based on prompts such as text, clicks, or bounding boxes.
Image segmentation with a bounding box prior
Victor Lempitsky, Pushmeet Kohli, Carsten Rother, and Toby Sharp · 2009
Earlier work this paper cites.
Attention is all you need
Vaswani Ashish · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy Alexey · 2020
Earlier work this paper cites.
Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Exploring plain vision transformer backbones for object detection
Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He · 2022
Earlier work this paper cites.
Lightweight convolutional network for automated photovoltaic defect detection
Arsalan Zahid, Muhammad Hussain, Richard Hill, and Hussain Al-Aqrabi · 2023
Cited alongside, same era.
Domain modelling for a lightweight convolutional network focused on automated exudate detection in retinal fundus images
Burcu Ataer Aydin, Muhammad Hussain, Richard Hill, and Hussain Al-Aqrabi · 2023
Cited alongside, same era.
A review on defect detection of electroluminescence-based photovoltaic cell surface images using computer vision
Tahir Hussain, Muhammad Hussain, Hussain Al-Aqrabi, Tariq Alsboui, and Richard Hill · 2023
Cited alongside, same era.
Segment anything – a foundation model for image segmentation, May 2023
Sovit Rath · 2023
Cited alongside, same era.
Segment anything
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
Cited alongside, same era.
Window attention is bugged: How not to interpolate position embeddings
Daniel Bolya, Chaitanya Ryali, Judy Hoffman, and Christoph Feichtenhofer · 2023
Later among the works it cites.
Sam 2: Segment anything in images and videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al · 2024
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Sam 2 – promptable segmentation for images and videos, July 2024
Sovit Rath · 2024
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Introducing sam 2: The next generation of meta segment anything model for videos and images, July 2024
Meta · 2024
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Introducing meta segment anything model 2: Use cases and improvements, July 2024
Datature · 2024
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Segment anything model (sam), April 2023
RoboFlow · 2023
Cited alongside, same era.
Top 5 use cases for segment anything model (sam)
James Gallagher · 2023
Cited alongside, same era.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu · 2024
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Rotary position embedding for vision transformer
Byeongho Heo, Song Park, Dongyoon Han, and Sangdoo Yun · 2024
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