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pro vyhledávání: '"Kang, Taegoo"'
Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object and demonstrates impressive zero-shot transfer performance with the guidance of prompts. However, there is currently a lack of comprehensive evalua
Externí odkaz:
http://arxiv.org/abs/2306.07713
Autor:
Zhang, Chaoning, Puspitasari, Fachrina Dewi, Zheng, Sheng, Li, Chenghao, Qiao, Yu, Kang, Taegoo, Shan, Xinru, Zhang, Chenshuang, Qin, Caiyan, Rameau, Francois, Lee, Lik-Hang, Bae, Sung-Ho, Hong, Choong Seon
Segment anything model (SAM) developed by Meta AI Research has recently attracted significant attention. Trained on a large segmentation dataset of over 1 billion masks, SAM is capable of segmenting any object on a certain image. In the original SAM
Externí odkaz:
http://arxiv.org/abs/2306.06211
Segment Anything Model (SAM) has attracted significant attention recently, due to its impressive performance on various downstream tasks in a zero-short manner. Computer vision (CV) area might follow the natural language processing (NLP) area to emba
Externí odkaz:
http://arxiv.org/abs/2305.00866
Autor:
Zhang, Mengchun, Qamar, Maryam, Kang, Taegoo, Jung, Yuna, Zhang, Chenshuang, Bae, Sung-Ho, Zhang, Chaoning
Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of articles on diffusion models increasing exponentially in the past few y
Externí odkaz:
http://arxiv.org/abs/2304.01565