1 code implementation • 5 Oct 2023 • Fei Hou, Xuhui Chen, Wencheng Wang, Hong Qin, Ying He
We show that the computed iso-surface is the boundary of the $r$-offset volume of the target zero level-set $S$, which is an orientable manifold, regardless of the topology of $S$.
2 code implementations • 23 May 2023 • Guotao Wang, Chenglizhao Chen, Aimin Hao, Hong Qin, Deng-Ping Fan
The main reason is that there always exist "blind zooms" when using HMD to collect fixations since the users cannot keep spinning their heads to explore the entire panoptic scene all the time.
1 code implementation • 25 Oct 2022 • Zhenyu Wu, Shuai Li, Chenglizhao Chen, Hong Qin, Aimin Hao
First, instead of using the vanilla convolution with fixed kernel sizes for the encoder design, we propose the dynamic pyramid convolution (DPConv), which dynamically selects the best-suited kernel sizes w. r. t.
no code implementations • 29 Mar 2022 • Hong Qin, Syed Tareq, William Torres, Megan Doman, Cleo Falvey, Jamaree Moore, Meng Hsiu Tsai, Yingfeng Wang, Azad Hossain, Mengjun Xie, Li Yang
We examined the cointegration of the effective reproductive rate, Rt, of the virus with the dewpoint at two meters, the temperature at two meters, Apple driving mobility, and Google workplace mobility measurements.
no code implementations • 15 Feb 2022 • Megan Doman, Jacob Motley, Hong Qin, Mengjun Xie, Li Yang
COVID-19 related policies were extensively politicized during the 2020 election year of the United States, resulting in polarizing viewpoints.
1 code implementation • 27 Dec 2021 • Guotao Wang, Chenglizhao Chen, Deng-Ping Fan, Aimin Hao, Hong Qin
Moreover, we distill knowledge from these regions to obtain complete new spatial-temporal-audio (STA) fixation prediction (FP) networks, enabling broad applications in cases where video tags are not available.
no code implementations • NeurIPS 2021 • Shoulong Zhang, Shuai Li, Aimin Hao, Hong Qin
Unlike conventional methods that learn knowledge embedding and regular patterns from encoded visual information, we propose to suppress the misunderstandings caused by appearance similarities and other perceptual confusion.
no code implementations • 25 Oct 2021 • Liping Zhang, Weijun Li, Linjun Sun, Lina Yu, Xin Ning, Xiaoli Dong, Jian Xu, Hong Qin
First, we propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms, where each term is an analytic function element.
1 code implementation • CVPR 2021 • Guotao Wang, Chenglizhao Chen, Deng-Ping Fan, Aimin Hao, Hong Qin
Thanks to the rapid advances in the deep learning techniques and the wide availability of large-scale training sets, the performances of video saliency detection models have been improving steadily and significantly.
no code implementations • 15 Dec 2020 • Zhenyu Wang, Hong Qin, Benjamin Sturdevant, Choong-Seock Chang
We compare the energy conservation property of the geometric PIC algorithm derived from the discrete variational principle with that of previous PIC methods on unstructured meshes.
Plasma Physics
no code implementations • 10 Aug 2020 • Zhen-Yu Wu, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
In sharp contrast to the state-of-the-art (SOTA) methods that focus on learning pixel-wise saliency in "single image" using perceptual clues mainly, our method has investigated the "object-level semantic ranks between multiple images", of which the methodology is more consistent with the real human attention mechanism.
no code implementations • 7 Aug 2020 • Zhen-Yu Wu, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets.
1 code implementation • 7 Aug 2020 • Zhen-Yu Wu, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections.
1 code implementation • 7 Aug 2020 • Xuehao Wang, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely.
1 code implementation • 7 Aug 2020 • Chenglizhao Chen, Guotao Wang, Chong Peng, Dingwen Zhang, Yuming Fang, Hong Qin
In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter.
1 code implementation • 7 Aug 2020 • Chenglizhao Chen, Jipeng Wei, Chong Peng, Hong Qin
The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D).
Ranked #16 on RGB-D Salient Object Detection on NJU2K
1 code implementation • 7 Aug 2020 • Xuehao Wang, Shuai Li, Chenglizhao Chen, Yuming Fang, Aimin Hao, Hong Qin
Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture.
no code implementations • 3 Aug 2020 • Liping Zhang, Weijun Li, Lina Yu, Xiaoli Dong, Linjun Sun, Xin Ning, Jian Xu, Hong Qin
The GmNet is then designed using Gaussian functions as neurons, with parameters that correspond to each of the parameters of GmFace in order to transform the problem of GmFace parameter solving into a network optimization problem of GmNet.
1 code implementation • 2 Aug 2020 • Yunxiao Li, Shuai Li, Chenglizhao Chen, Aimin Hao, Hong Qin
With the rapid development of deep learning techniques, image saliency deep models trained solely by spatial information have occasionally achieved detection performance for video data comparable to that of the models trained by both spatial and temporal information.
no code implementations • 11 Jul 2020 • Dongbo Zhang, Zheng Fang, Xuequan Lu, Hong Qin, Antonio Robles-Kelly, Chao Zhang, Ying He
3D human segmentation has seen noticeable progress in re-cent years.
no code implementations • 17 Apr 2020 • Jianyuan Xiao, Hong Qin
The code has been applied to carry out whole-device 6D kinetic simulation studies of tokamak physics.
Plasma Physics Computational Physics
no code implementations • 14 Feb 2020 • Dongbo Zhang, Xuequan Lu, Hong Qin, Ying He
In this paper, we propose a novel deep learning approach that automatically and robustly filters point clouds with removing noise and preserving sharp features and geometric details.
Graphics
no code implementations • 22 Oct 2019 • Hong Qin
A method for machine learning and serving of discrete field theories in physics is developed.
1 code implementation • 25 Aug 2017 • Junting Ye, Shuchu Han, Yifan Hu, Baris Coskun, Meizhu Liu, Hong Qin, Steven Skiena
Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population.
no code implementations • 30 Jun 2017 • Ting Lan, Jian Liu, Hong Qin
To obtain the general expressions of performance measures based on the preferences of tasks, the mapping relations between performance of TDGS method about physical similarity and correctness of data sequences are investigated by probability theory in this paper.
no code implementations • 30 Jun 2017 • Jian Liu, Ting Lan, Hong Qin
Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class$-$imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices.
no code implementations • 13 May 2017 • Shuchu Han, Hao Huang, Hong Qin
The redundant features existing in high dimensional datasets always affect the performance of learning and mining algorithms.
1 code implementation • 24 May 2013 • Hong Qin
Our model predicts that the rate of aging, defined by the Gompertz coefficient, is approximately proportional to the average number of active interactions per gene and that the stochastic heterogeneity of gene interactions is an important factor in the dynamics of the aging process.