no code implementations • 18 Mar 2024 • Rao Fu, Jingyu Liu, Xilun Chen, Yixin Nie, Wenhan Xiong
This paper introduces Scene-LLM, a 3D-visual-language model that enhances embodied agents' abilities in interactive 3D indoor environments by integrating the reasoning strengths of Large Language Models (LLMs).
no code implementations • 18 Jan 2024 • Lars Ericson, Xuejun Zhu, Xusi Han, Rao Fu, Shuang Li, Steve Guo, Ping Hu
The objectives for financial time series generation are to generate synthetic data paths with good variety, and similar distribution and dynamics to the original historical data.
no code implementations • 11 Dec 2023 • Rao Fu, Zehao Wen, Zichen Liu, Srinath Sridhar
Inspired by cognitive theories, we introduce AnyHome, a framework that translates any text into well-structured and textured indoor scenes at a house-scale.
1 code implementation • 22 Jul 2023 • Cheng Wen, Baosheng Yu, Rao Fu, DaCheng Tao
A generative model for high-fidelity point clouds is of great importance in synthesizing 3d environments for applications such as autonomous driving and robotics.
1 code implementation • 8 Jul 2023 • Rao Fu, Cheng Wen, Qian Li, Xiao Xiao, Pierre Alliez
This paper proposes BPNet, a novel end-to-end deep learning framework to learn B\'ezier primitive segmentation on 3D point clouds.
1 code implementation • 6 Apr 2023 • Yu Zhang, Xiaoguang Di, Junde Wu, Rao Fu, Yong Li, Yue Wang, Yanwu Xu, Guohui YANG, Chunhui Wang
In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions.
no code implementations • CVPR 2023 • Aditya Sanghi, Rao Fu, Vivian Liu, Karl Willis, Hooman Shayani, Amir Hosein Khasahmadi, Srinath Sridhar, Daniel Ritchie
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes.
2 code implementations • 1 Nov 2022 • Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, Yehui Yang, Haoyi Xiong, Huiying Liu, Yanwu Xu
Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff.
1 code implementation • 5 Aug 2022 • Junde Wu, Yu Zhang, Rao Fu, Yuanpei Liu, Jing Gao
Then, to ensure that the method adapts to the dynamic and unseen person flow, we propose Graph Convolutional Network (GCN) with a simple Nearest Neighbor (NN) strategy to accurately cluster the instances of CSG.
1 code implementation • 19 Jul 2022 • Rao Fu, Xiao Zhan, YiWen Chen, Daniel Ritchie, Srinath Sridhar
Results show that our method can generate shapes consistent with text descriptions, and shapes evolve gradually as more phrases are added.
no code implementations • 12 Jun 2022 • Trevor Houchens, Cheng-You Lu, Shivam Duggal, Rao Fu, Srinath Sridhar
We propose Omnidirectional Distance Fields (ODFs), a new 3D shape representation that encodes geometry by storing the depth to the object's surface from any 3D position in any viewing direction.
2 code implementations • NeurIPS 2021 • Yuhui Yuan, Rao Fu, Lang Huang, WeiHong Lin, Chao Zhang, Xilin Chen, Jingdong Wang
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
1 code implementation • 18 Oct 2021 • Yuhui Yuan, Rao Fu, Lang Huang, WeiHong Lin, Chao Zhang, Xilin Chen, Jingdong Wang
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
Ranked #3 on Pose Estimation on AIC
no code implementations • 19 Oct 2020 • Yang Yang, Junmei Hao, Canjia Li, Zili Wang, Jingang Wang, Fuzheng Zhang, Rao Fu, Peixu Hou, Gong Zhang, Zhongyuan Wang
Existing work on tip generation does not take query into consideration, which limits the impact of tips in search scenarios.
1 code implementation • 2 Oct 2020 • Rao Fu, Jie Yang, Jiawei Sun, Fang-Lue Zhang, Yu-Kun Lai, Lin Gao
Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures.
1 code implementation • 22 Jul 2020 • Junde Wu, Shuang Yu, WenTing Chen, Kai Ma, Rao Fu, Hanruo Liu, Xiaoguang Di, Yefeng Zheng
Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts.
1 code implementation • 29 Aug 2019 • Junde Wu, Rao Fu
The question is: Is there existan attack that can meet all these requirements?
no code implementations • 25 Apr 2019 • Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto
Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation.