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Segment Anything Model (SAM) has gained considerable interest in recent times for its remarkable performance and has emerged as a foundational model in computer vision.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
Earlier work this paper cites.
Clearing the skies: A deep network architecture for single-image rain removal
Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley · 2017
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Earlier work this paper cites.
Dawn: vehicle detection in adverse weather nature dataset
Mourad A Kenk and Mahmoud Hassaballah · 2020
Earlier work this paper cites.
Vehicle detection and tracking in adverse weather using a deep learning framework
Mahmoud Hassaballah, Mourad A Kenk, Khan Muhammad, and Shervin Minaee · 2020
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Reviewnet: A fast and resource optimized network for enabling safe autonomous driving in hazy weather conditions
Aryan Mehra, Murari Mandal, Pratik Narang, and Vinay Chamola · 2020
Earlier work this paper cites.
Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 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.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
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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Segment anything in medical images
Jun Ma and Bo Wang · 2023
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Grounded segment anything, 2023
IDEA-Research · 2023
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Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al · 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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Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
Cited alongside, same era.
Autonomous driving in adverse weather conditions: A survey
Yuxiao Zhang, Alexander Carballo, Hanting Yang, and Kazuya Takeda · 2021
Cited alongside, same era.
Izzeddin Teeti, Valentina Musat, Salman Khan, Alexander Rast, Fabio Cuzzolin, and Andrew Bradley · 2022
Cited alongside, same era.
Generative ai meets 3d: A survey on text-to-3d in aigc era
Chenghao Li, Chaoning Zhang, Atish Waghwase, Lik-Hang Lee, Francois Rameau, Yang Yang, Sung-Ho Bae, and Choong Seon Hong · 2023
Cited alongside, same era.
A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need?
Chaoning Zhang, Chenshuang Zhang, Sheng Zheng, Yu Qiao, Chenghao Li, Mengchun Zhang, Sumit Kumar Dam, Chu Myaet Thwal, Ye Lin Tun, Le Luang Huy, et al
Cited in the paper.
Text-to-image diffusion models in generative ai: A survey
Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, and In So Kweon
Cited in the paper.
A survey on audio diffusion models: Text to speech synthesis and enhancement in generative ai
Chenshuang Zhang, Chaoning Zhang, Sheng Zheng, Mengchun Zhang, Maryam Qamar, Sung-Ho Bae, and In So Kweon
Cited in the paper.
One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era
Chaoning Zhang, Chenshuang Zhang, Chenghao Li, Yu Qiao, Sheng Zheng, Sumit Kumar Dam, Mengchun Zhang, Jung Uk Kim, Seong Tae Kim, Jinwoo Choi, et al
Cited in the paper.
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Benchmarking robustness of 3d object detection to common corruptions in autonomous driving
Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, and Jun Zhu · 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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