Search Results for author: Di Guo

Found 20 papers, 2 papers with code

Multi-Agent Embodied Question Answering in Interactive Environments

no code implementations ECCV 2020 Sinan Tan, Weilai Xiang, Huaping Liu, Di Guo, Fuchun Sun

We investigate a new AI task --- Multi-Agent Interactive Question Answering --- where several agents explore the scene jointly in interactive environments to answer a question.

3D Reconstruction Embodied Question Answering +1

Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge

no code implementations20 Sep 2021 Xinzhu Liu, Di Guo, Huaping Liu, Fuchun Sun

In this paper, we propose the multi-agent visual semantic navigation, in which multiple agents collaborate with others to find multiple target objects.

Efficient Exploration

Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization

no code implementations24 Jul 2021 Xinlin Zhang, Hengfa Lu, Di Guo, Zongying Lai, Huihui Ye, Xi Peng, Bo Zhao, Xiaobo Qu

The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time.

MRI Reconstruction

XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI

no code implementations18 Apr 2021 Yirong Zhou, Chen Qian, Yi Guo, Zi Wang, Jian Wang, Biao Qu, Di Guo, Yongfu You, Xiaobo Qu

Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).

Image Reconstruction

Denoising Single Voxel Magnetic Resonance Spectroscopy with Deep Learning on Repeatedly Sampled In Vivo Data

no code implementations26 Jan 2021 Wanqi Hu, Dicheng Chen, Tianyu Qiu, Hao Chen, Xi Chen, Lin Yang, Gen Yan, Di Guo, Xiaobo Qu

Methods: By exploring the multiple sampled data, a deep learning denoising approach is proposed to learn a mapping from the low SNR signal to the high SNR one.

Denoising

XCloud-MoDern: An Artificial Intelligence Cloud for Accelerated NMR Spectroscopy

1 code implementation29 Dec 2020 Zi Wang, Di Guo, Zhangren Tu, Yihui Huang, Yirong Zhou, Jian Wang, Liubin Feng, Donghai Lin, Yongfu You, Tatiana Agback, Vladislav Orekhov, Xiaobo Qu

For accelerated multi-dimensional NMR spectroscopy, non-uniform sampling is a powerful approach but requires sophisticated algorithms to reconstruct undersampled data.

Unsupervised Representation Learning by Invariance Propagation

no code implementations NeurIPS 2020 Feng Wang, Huaping Liu, Di Guo, Sun Fuchun

In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.

Contrastive Learning Fine-tuning +2

Fault-Aware Robust Control via Adversarial Reinforcement Learning

no code implementations17 Nov 2020 Fan Yang, Chao Yang, Di Guo, Huaping Liu, Fuchun Sun

Robots have limited adaptation ability compared to humans and animals in the case of damage.

Fine-tuning

Unsupervised Representation Learning by InvariancePropagation

1 code implementation7 Oct 2020 Feng Wang, Huaping Liu, Di Guo, Fuchun Sun

In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.

Contrastive Learning Fine-tuning +2

Exponential Signal Reconstruction with Deep Hankel Matrix Factorization

no code implementations13 Jul 2020 Yihui Huang, Jinkui Zhao, Zi Wang, Di Guo, Xiaobo Qu

Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing.

Towards Embodied Scene Description

no code implementations30 Apr 2020 Sinan Tan, Huaping Liu, Di Guo, Xin-Yu Zhang, Fuchun Sun

Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment.

Imitation Learning

MQA: Answering the Question via Robotic Manipulation

no code implementations10 Mar 2020 Yuhong Deng, Di Guo, Xiaofeng Guo, Naifu Zhang, Huaping Liu, Fuchun Sun

In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given question.

Imitation Learning Question Answering +1

Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy

no code implementations13 Jan 2020 Dicheng Chen, Zi Wang, Di Guo, Vladislav Orekhov, Xiaobo Qu

In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.

pISTA-SENSE-ResNet for Parallel MRI Reconstruction

no code implementations24 Sep 2019 Tieyuan Lu, Xinlin Zhang, Yihui Huang, Yonggui Yang, Gang Guo, Lijun Bao, Feng Huang, Di Guo, Xiaobo Qu

Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time.

MRI Reconstruction

A Guaranteed Convergence Analysis for the Projected Fast Iterative Soft-Thresholding Algorithm in Parallel MRI

no code implementations17 Sep 2019 Xinlin Zhang, Hengfa Lu, Di Guo, Lijun Bao, Feng Huang, Qin Xu, Xiaobo Qu

The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging.

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

no code implementations9 Apr 2019 Xiaobo Qu, Yihui Huang, Hengfa Lu, Tianyu Qiu, Di Guo, Tatiana Agback, Vladislav Orekhov, Zhong Chen

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time.

Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals

no code implementations6 Apr 2016 Jiaxi Ying, Hengfa Lu, Qingtao Wei, Jian-Feng Cai, Di Guo, Jihui Wu, Zhong Chen, Xiaobo Qu

Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.

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