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of 10
pro vyhledávání: '"Jin, Yuhe"'
Depression is a very common but serious mood disorder.In this paper, We built a generative detection network(GDN) in accordance with three physiological laws. Our aim is that we expect the neural network to learn the relevant brain activity based on
Externí odkaz:
http://arxiv.org/abs/2402.09421
Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality, spatially consistent new views. While recent methods fo
Externí odkaz:
http://arxiv.org/abs/2312.01305
We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomp
Externí odkaz:
http://arxiv.org/abs/2212.01735
Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e.g. elbow, digit, abstract geometric shape) appears only once in an image. This greatly limits their applicability, as each instance mu
Externí odkaz:
http://arxiv.org/abs/2206.08460
We introduce layered controllable video generation, where we, without any supervision, decompose the initial frame of a video into foreground and background layers, with which the user can control the video generation process by simply manipulating t
Externí odkaz:
http://arxiv.org/abs/2111.12747
Autor:
Jin, Yuhe, Mishkin, Dmytro, Mishchuk, Anastasiia, Matas, Jiri, Fua, Pascal, Yi, Kwang Moo, Trulls, Eduard
We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric. Our pipeline's modular structure allows easy integr
Externí odkaz:
http://arxiv.org/abs/2003.01587
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most
Externí odkaz:
http://arxiv.org/abs/1811.10725
Autor:
Xiong, Yaxuan, Song, Chaoyu, Ren, Jing, Jin, Yuhe, Nie, Binjian, Xu, Qian, Wu, Yuting, Li, Chuan, Li, Haimeng, Ding, Yulong
Publikováno v:
In Process Safety and Environmental Protection June 2022 162:346-356
Publikováno v:
In Renewable Energy December 2017 114 Part A:166-179
Autor:
Jin, Yuhe1 (AUTHOR), Mishkin, Dmytro2 (AUTHOR), Mishchuk, Anastasiia3 (AUTHOR), Matas, Jiri2 (AUTHOR), Fua, Pascal3 (AUTHOR), Yi, Kwang Moo1 (AUTHOR), Trulls, Eduard4 (AUTHOR) trulls@google.com
Publikováno v:
International Journal of Computer Vision. Feb2021, Vol. 129 Issue 2, p517-547. 31p.