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Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: \textbf{segment anything (SegAny)}, which utilizes a certain point to predict the mask for a single object of interest, and \textbf{segment everything (SegEvery)}, which predicts the masks for all objects on the image.
A review of semantic segmentation using deep neural networks
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Lvis: A dataset for large vocabulary instance segmentation
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On the opportunities and risks of foundation models
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Learning transferable visual models from natural language supervision
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Detreg: Unsupervised pretraining with region priors for object detection
Amir Bar, Xin Wang, Vadim Kantorov, Colorado J Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, and Amir Globerson · 2022
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Any-speaker adaptive text-to-speech synthesis with diffusion models
Minki Kang, Dongchan Min, and Sung Ju Hwang · 2022
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Learning open-world object proposals without learning to classify
Dahun Kim, Tsung-Yi Lin, Anelia Angelova, In So Kweon, and Weicheng Kuo · 2022
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Class-agnostic object detection with multi-modal transformer
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, and Ming-Hsuan Yang · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Open-world instance segmentation: Exploiting pseudo ground truth from learned pairwise affinity
Weiyao Wang, Matt Feiszli, Heng Wang, Jitendra Malik, and Du Tran · 2022
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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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Segment anything in medical images
Jun Ma and Bo Wang · 2023
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Towards open-world segmentation of parts
Tai-Yu Pan, Qing Liu, Wei-Lun Chao, and Brian Price · 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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Anything-3d: Towards single-view anything reconstruction in the wild
Qiuhong Shen, Xingyi Yang, and Xinchao Wang · 2023
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Dual temperature helps contrastive learning without many negative samples: Towards understanding and simplifying moco
Chaoning Zhang, Kang Zhang, Trung X. Pham, Changdong Yoo, and In-So Kweon · 2022
Cited alongside, same era.
Semantic-segment-anything, 2023
Jiaqi Chen, Zeyu Yang, and Li Zhang · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Grounded segment anything, 2023
IDEA-Research · 2023
Cited alongside, same era.
Segment anything in high quality
Lei Ke, Mingqiao Ye, Martin Danelljan, Yifan Liu, Yu-Wing Tai, Chi-Keung Tang, and Fisher Yu · 2023
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
Cited alongside, same era.
Semantic-sam: Segment and recognize anything at any granularity
Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, and Jianfeng Gao · 2023
Cited alongside, same era.
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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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Exploring transformers for open-world instance segmentation
Jiannan Wu, Yi Jiang, Bin Yan, Huchuan Lu, Zehuan Yuan, and Ping Luo · 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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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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Xu Zhao, Wenchao Ding, Yongqi An, Yinglong Du, Tao Yu, Min Li, Ming Tang, and Jinqiao Wang · 2023
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Black-box targeted adversarial attack on segment anything (sam)
Sheng Zheng and Chaoning Zhang · 2023
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Segment and track anything, 2023
Zxyang · 2023
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