no code implementations • 19 Apr 2024 • Wenkai Liu, Tao Guan, Bin Zhu, Lili Ju, Zikai Song, Dan Li, Yuesong Wang, Wei Yang
In the domain of 3D scene representation, 3D Gaussian Splatting (3DGS) has emerged as a pivotal technology.
no code implementations • 27 Jan 2023 • Zezhong Zhang, Feng Bao, Lili Ju, Guannan Zhang
Transfer learning for partial differential equations (PDEs) is to develop a pre-trained neural network that can be used to solve a wide class of PDEs.
1 code implementation • 9 Dec 2021 • Xinyi Wu, Zhenyao Wu, Yuhang Lu, Lili Ju, Song Wang
In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training.
One-shot Unsupervised Domain Adaptation Semantic Segmentation +2
2 code implementations • 2 Dec 2021 • Yuankai Teng, Zhu Wang, Lili Ju, Anthony Gruber, Guannan Zhang
Our method contains two major components: one is the pseudo-reversible neural network (PRNN) module that effectively transforms high-dimensional input variables to low-dimensional active variables, and the other is the synthesized regression module for approximating function values based on the transformed data in the low-dimensional space.
1 code implementation • 5 Oct 2021 • Anthony Gruber, Max Gunzburger, Lili Ju, Zhu Wang
The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems.
1 code implementation • 23 May 2021 • Yuankai Teng, XiaoPing Zhang, Zhu Wang, Lili Ju
Partial differential equations are often used to model various physical phenomena, such as heat diffusion, wave propagation, fluid dynamics, elasticity, electrodynamics and image processing, and many analytic approaches or traditional numerical methods have been developed and widely used for their solutions.
1 code implementation • 29 Apr 2021 • Anthony Gruber, Max Gunzburger, Lili Ju, Yuankai Teng, Zhu Wang
A dimension reduction method based on the "Nonlinear Level set Learning" (NLL) approach is presented for the pointwise prediction of functions which have been sparsely sampled.
1 code implementation • CVPR 2021 • Xinyi Wu, Zhenyao Wu, Hao Guo, Lili Ju, Song Wang
We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects.
Ranked #6 on Semantic Segmentation on Nighttime Driving
no code implementations • 10 Sep 2018 • Jianfeng Zhang, Liezhuo Zhang, Yuankai Teng, Xiao-Ping Zhang, Song Wang, Lili Ju
Binary image segmentation plays an important role in computer vision and has been widely used in many applications such as image and video editing, object extraction, and photo composition.