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of 39
pro vyhledávání: '"Shu, Michelle"'
Text-conditioned diffusion models can generate impressive images, but fall short when it comes to fine-grained control. Unlike direct-editing tools like Photoshop, text conditioned models require the artist to perform "prompt engineering," constructi
Externí odkaz:
http://arxiv.org/abs/2404.03145
Autor:
Shu, Michelle, Bowen, Richard Strong, Herrmann, Charles, Qi, Gengmo, Santacatterina, Michele, Zabih, Ramin
Time-to-event analysis is an important statistical tool for allocating clinical resources such as ICU beds. However, classical techniques like the Cox model cannot directly incorporate images due to their high dimensionality. We propose a deep learni
Externí odkaz:
http://arxiv.org/abs/2108.09641
Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot Segmentation (FS-Seg) tackles this problem with many constraints. In this
Externí odkaz:
http://arxiv.org/abs/2010.05210
State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation is thus proposed to tackle this problem by learning a model that quick
Externí odkaz:
http://arxiv.org/abs/2008.01449
Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands has the potential to pave the way to systems that can learn from petaby
Externí odkaz:
http://arxiv.org/abs/2006.06669
Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g. autonomous driving), it is the worst case performance that matters mor
Externí odkaz:
http://arxiv.org/abs/1911.11230
Autor:
Tian, Zhuotao, Zhao, Hengshuang, Shu, Michelle, Wang, Jiaze, Li, Ruiyu, Shen, Xiaoyong, Jia, Jiaya
Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection. In this paper, we propose a Region Refinement Network (RRN), which recurrently filters redundant information and explicitly models
Externí odkaz:
http://arxiv.org/abs/1906.11443
Autor:
Chen, Ying-Cong, Lin, Huaijia, Shu, Michelle, Li, Ruiyu, Tao, Xin, Ye, Yangang, Shen, Xiaoyong, Jia, Jiaya
Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks. With a wide variety of user preferences, facial expressions, and accessories, a general and flexible model is n
Externí odkaz:
http://arxiv.org/abs/1803.05576
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