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pro vyhledávání: '"Ryou, Serim"'
Autor:
Ryou, Serim
The world surrounding us is full of structured entities. Scenes can be structured as the sum of objects arranged in space, objects can be decomposed into parts, and even small molecules are composed of atoms. As humans can organize and structure many
Autor:
Sun, Jennifer J., Karashchuk, Lili, Dravid, Amil, Ryou, Serim, Fereidooni, Sonia, Tuthill, John, Katsaggelos, Aggelos, Brunton, Bingni W., Gkioxari, Georgia, Kennedy, Ann, Yue, Yisong, Perona, Pietro
Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised keypoint discovery is a promising strategy for estimating 3D poses wi
Externí odkaz:
http://arxiv.org/abs/2212.07401
Autor:
Sun, Jennifer J., Ryou, Serim, Goldshmid, Roni, Weissbourd, Brandon, Dabiri, John, Anderson, David J., Kennedy, Ann, Yue, Yisong, Perona, Pietro
We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method, Behavioral Keypoin
Externí odkaz:
http://arxiv.org/abs/2112.05121
Autor:
Ryou, Serim, Perona, Pietro
In this paper, we propose a method for keypoint discovery from a 2D image using image-level supervision. Recent works on unsupervised keypoint discovery reliably discover keypoints of aligned instances. However, when the target instances have high vi
Externí odkaz:
http://arxiv.org/abs/2109.13423
Autor:
Ryou, Serim, Maser, Michael R., Cui, Alexander Y., DeLano, Travis J., Yue, Yisong, Reisman, Sarah E.
We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different G
Externí odkaz:
http://arxiv.org/abs/2007.04275
We propose a novel loss function that dynamically rescales the cross entropy based on prediction difficulty regarding a sample. Deep neural network architectures in image classification tasks struggle to disambiguate visually similar objects. Likewis
Externí odkaz:
http://arxiv.org/abs/1909.11155
Akademický článek
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Autor:
Sun JJ; Caltech., Ryou S; Caltech., Goldshmid RH; Caltech., Weissbourd B; Caltech., Dabiri JO; Caltech., Anderson DJ; Caltech., Kennedy A; Northwestern University., Yue Y; Caltech.; Argo AI., Perona P; Caltech.
Publikováno v:
Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition [Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit] 2022 Jun; Vol. 2022, pp. 2161-2170. Date of Electronic Publication: 2022 Sep 27.
