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As Segment Anything Model (SAM) becomes a popular foundation model in computer vision, its adversarial robustness has become a concern that cannot be ignored.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
Ishan Misra, C Lawrence Zitnick, and Martial Hebert · 2016
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In defense of the triplet loss for person re-identification
Alexander Hermans, Lucas Beyer, and Bastian Leibe · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Art of singular vectors and universal adversarial perturbations
Valentin Khrulkov and Ivan Oseledets · 2018
Earlier work this paper cites.
Playing the game of universal adversarial perturbations
Julien Perolat, Mateusz Malinowski, Bilal Piot, and Olivier Pietquin · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Double targeted universal adversarial perturbations
Philipp Benz, Chaoning Zhang, Tooba Imtiaz, and In So Kweon · 2020
Cited alongside, same era.
Learning robust representations via multi-view information bottleneck
Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, and Zeynep Akata · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
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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magic-copy, 2023
Kevmo · 2023
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Anything 3d, 2023
Adamdad · 2023
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3d box segment anything, 2023
Yukang Chen · 2023
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Robustness of sam: Segment anything under corruptions and beyond
Yu Qiao, Chaoning Zhang, Taegoo Kang, Donghun Kim, Shehbaz Tariq, Chenshuang Zhang, and Choong Seon Hong · 2023
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Can sam segment anything? when sam meets camouflaged object detection
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Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 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.
Data-free universal adversarial perturbation and black-box attack
Chaoning Zhang, Philipp Benz, Adil Karjauv, and In So Kweon · 2021
Cited alongside, same era.
Understanding the behaviour of contrastive loss
Feng Wang and Huaping Liu · 2021
Cited alongside, same era.
Any-speaker adaptive text-to-speech synthesis with diffusion models
Minki Kang, Dongchan Min, and Sung Ju Hwang · 2022
Cited alongside, same era.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
Cited alongside, same era.
Lv Tang, Haoke Xiao, and Bo Li · 2023
Closest in time.
Segment anything in medical images
Jun Ma and Bo Wang · 2023
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Segment anything model (sam) meets glass: Mirror and transparent objects cannot be easily detected
Dongsheng Han, Chaoning Zhang, Yu Qiao, Maryam Qamar, Yuna Jung, SeungKyu Lee, Sung-Ho Bae, and Choong Seon Hong · 2023
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Anything-3d: Towards single-view anything reconstruction in the wild
Qiuhong Shen, Xingyi Yang, and Xinchao Wang · 2023
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Grounded segment anything, 2023
IDEA-Research · 2023
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Semantic-segment-anything, 2023
Jiaqi Chen, Zeyu Yang, and Li Zhang · 2023
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segment anything with clip, 2023
Curt Park · 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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Track anything: Segment anything meets videos
Jinyu Yang, Mingqi Gao, Zhe Li, Shang Gao, Fangjing Wang, and Feng Zheng · 2023
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Segment and track anything, 2023
Zxyang · 2023
Closest in time.