J
Jiayun Wang
Researcher at University of California, Berkeley
Publications - 38
Citations - 1352
Jiayun Wang is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 7, co-authored 16 publications receiving 538 citations. Previous affiliations of Jiayun Wang include Vision-Sciences, Inc. & International Computer Science Institute.
Papers
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Proceedings ArticleDOI
Large-Scale Long-Tailed Recognition in an Open World
TL;DR: An integrated OLTR algorithm is developed that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world.
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Large-Scale Long-Tailed Recognition in an Open World
TL;DR: Open Long-Tailed Recognition (OLTR) as mentioned in this paper maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world.
Proceedings ArticleDOI
Orthogonal Convolutional Neural Networks
TL;DR: The proposed orthogonal convolution requires no additional parameters and little computational overhead and consistently outperforms the kernel orthogonality alternative on a wide range of tasks such as image classification and inpainting under supervised, semi-supervised and unsupervised settings.
Journal ArticleDOI
Insights and approaches using deep learning to classify wildlife.
Zhongqi Miao,Kaitlyn M. Gaynor,Jiayun Wang,Ziwei Liu,Oliver Muellerklein,Oliver Muellerklein,Mohammad Sadegh Norouzzadeh,Alex McInturff,Rauri C. K. Bowie,Ran Nathan,Stella X. Yu,Stella X. Yu,Wayne M. Getz +12 more
TL;DR: Light is shed on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications of wildlife species from camera-trap data, and presents dataset biases that were revealed by these extracted features.
Journal ArticleDOI
A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images
Jiayun Wang,Jiayun Wang,Thao N. Yeh,Rudrasis Chakraborty,Stella X. Yu,Stella X. Yu,Meng C. Lin +6 more
TL;DR: The proposed deep learning approach can automatically segment the total eyelid and meibomian gland atrophy regions, as well as compute percent atrophy with high accuracy and consistency.