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Yong Guo - IEEE Xplore Author Profile
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Yong Guo

Publications
16
Citations
562
Publications by Year
20192024
Co-Authors:
Show All Co-Authors (48)
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Yong Guo

Affiliation

Huawei Noah's Ark Lab
Montreal
QC, Canada

Publication Topics

Biography

Yong Guo received the bachelor’s and Ph.D. degrees from the South China University of Technology in 2016 and 2021, respectively. He is currently a Post-Doctoral Researcher with the Max Planck Institute for Informatics. His research interests include deep learning and computer vision.(Based on document published on 30 April 2024).
Publications
16
Citations
562
Publications by Year
20192024
Co-Authors:
Show All Co-Authors (48)
Author's Published Works

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DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model

Zhenhua Xu;Yujia Zhang;Enze Xie;Zhen Zhao;Yong Guo;Kwan-Yee K. Wong;Zhenguo Li;Hengshuang Zhao

IEEE Robotics and Automation Letters
Year: 2024 | Volume: 9, Issue: 10 | Journal Article |
Cited by: Papers (38)
Multimodallarge language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-textual data, including images and videos. This study seeks to extend the application of MLLMs to the realm of autonomous driving by introducing DriveGPT4, a novel interpretable end-to-end autonomous driving system based on L...Show More

DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model

Zhenhua Xu;Yujia Zhang;Enze Xie;Zhen Zhao;Yong Guo;Kwan-Yee K. Wong;Zhenguo Li;Hengshuang Zhao

Year: 2024 | Volume: 9, Issue: 10 | Journal Article |

Enhanced Long-Tailed Recognition With Contrastive CutMix Augmentation

Haolin Pan;Yong Guo;Mianjie Yu;Jian Chen

IEEE Transactions on Image Processing
Year: 2024 | Volume: 33 | Journal Article |
Cited by: Papers (1)
Real-world data often follows a long-tailed distribution, where a few head classes occupy most of the data and a large number of tail classes only contain very limited samples. In practice, deep models often show poor generalization performance on tail classes due to the imbalanced distribution. To tackle this, data augmentation has become an effective way by synthesizing new samples for tail clas...Show More

Enhanced Long-Tailed Recognition With Contrastive CutMix Augmentation

Haolin Pan;Yong Guo;Mianjie Yu;Jian Chen

Year: 2024 | Volume: 33 | Journal Article |

Towards Lightweight Super-Resolution With Dual Regression Learning

Yong Guo;Mingkui Tan;Zeshuai Deng;Jingdong Wang;Qi Chen;Jiezhang Cao;Yanwu Xu;Jian Chen

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 12 | Journal Article |
Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images. However, the SR problem is typically an ill-posed problem and existing methods would come with several limitations. First, the possible mapping space of SR can be extremely large since there may exist many different HR...Show More

Towards Lightweight Super-Resolution With Dual Regression Learning

Yong Guo;Mingkui Tan;Zeshuai Deng;Jingdong Wang;Qi Chen;Jiezhang Cao;Yanwu Xu;Jian Chen

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

Automated Dominative Subspace Mining for Efficient Neural Architecture Search

Yaofo Chen;Yong Guo;Daihai Liao;Fanbing Lv;Hengjie Song;James Tin-Yau Kwok;Mingkui Tan

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 10 | Journal Article |
Cited by: Papers (1)
Neural Architecture Search (NAS) aims to automatically find effective architectures within a predefined search space. However, the search space is often extremely large. As a result, directly searching in such a large search space is non-trivial and also very time-consuming. To address the above issues, in each search step, we seek to limit the search space to a small but effective subspace to boo...Show More

Automated Dominative Subspace Mining for Efficient Neural Architecture Search

Yaofo Chen;Yong Guo;Daihai Liao;Fanbing Lv;Hengjie Song;James Tin-Yau Kwok;Mingkui Tan

Year: 2024 | Volume: 34, Issue: 10 | Journal Article |

Robustifying Token Attention for Vision Transformers

Yong Guo;David Stutz;Bernt Schiele

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (11)
Despite the success of vision transformers (ViTs), they still suffer from significant drops in accuracy in the presence of common corruptions, such as noise or blur. Interestingly, we observe that the attention mechanism of ViTs tends to rely on few important tokens, a phenomenon we call token overfocusing. More critically, these tokens are not robust to corruptions, often leading to highly diverg...Show More

Robustifying Token Attention for Vision Transformers

Yong Guo;David Stutz;Bernt Schiele

Year: 2023 | Conference Paper |

Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images

Bingna Xu;Yong Guo;Luoqian Jiang;Mianjie Yu;Jian Chen

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Deep networks have achieved great success in image rescaling (IR) task that seeks to learn the optimal downscaled representations, i.e., low-resolution (LR) images, to reconstruct the original high-resolution (HR) images. Compared with super-resolution methods that consider a fixed downscaling scheme, e.g., bicubic, IR often achieves significantly better reconstruction performance thanks to the le...Show More

Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images

Bingna Xu;Yong Guo;Luoqian Jiang;Mianjie Yu;Jian Chen

Year: 2023 | Conference Paper |

Pareto-aware Neural Architecture Generation for Diverse Computational Budgets

Yong Guo;Yaofo Chen;Yin Zheng;Qi Chen;Peilin Zhao;Junzhou Huang;Jian Chen;Mingkui Tan

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world applications. To achieve this goal, existing methods often perform an independent architecture search process for each target budget, which is very inefficient yet unnecessary. More critically, these independent search ...Show More

Pareto-aware Neural Architecture Generation for Diverse Computational Budgets

Yong Guo;Yaofo Chen;Yin Zheng;Qi Chen;Peilin Zhao;Junzhou Huang;Jian Chen;Mingkui Tan

Year: 2023 | Conference Paper |

Towards Accurate and Compact Architectures via Neural Architecture Transformer

Yong Guo;Yin Zheng;Mingkui Tan;Qi Chen;Zhipeng Li;Jian Chen;Peilin Zhao;Junzhou Huang

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2022 | Volume: 44, Issue: 10 | Journal Article |
Cited by: Papers (27)
Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods. However, even a well-designed/searched architecture may still contain many nonsignificant or redundant modules/operations (e.g., some intermediate convolution or p...Show More

Towards Accurate and Compact Architectures via Neural Architecture Transformer

Yong Guo;Yin Zheng;Mingkui Tan;Qi Chen;Zhipeng Li;Jian Chen;Peilin Zhao;Junzhou Huang

Year: 2022 | Volume: 44, Issue: 10 | Journal Article |

Discrimination-Aware Network Pruning for Deep Model Compression

Jing Liu;Bohan Zhuang;Zhuangwei Zhuang;Yong Guo;Junzhou Huang;Jinhui Zhu;Mingkui Tan

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2022 | Volume: 44, Issue: 8 | Journal Article |
Cited by: Papers (57)
We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks. Existing pruning methods either train from scratch with sparsity constraints or minimize the reconstruction error between the feature maps of the pre-trained models and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expen...Show More

Discrimination-Aware Network Pruning for Deep Model Compression

Jing Liu;Bohan Zhuang;Zhuangwei Zhuang;Yong Guo;Junzhou Huang;Jinhui Zhu;Mingkui Tan

Year: 2022 | Volume: 44, Issue: 8 | Journal Article |

Deep View Synthesis via Self-Consistent Generative Network

Zhuoman Liu;Wei Jia;Ming Yang;Peiyao Luo;Yong Guo;Mingkui Tan

IEEE Transactions on Multimedia
Year: 2022 | Volume: 24 | Journal Article |
Cited by: Papers (9)
View synthesis aims to produce unseen views from a set of views captured by two or more cameras at different positions. This task is non-trivial since it is hard to conduct pixel-level matching among different views. To address this issue, most existing methods seek to exploit the geometric information to match pixels. However, when the distinct cameras have a large baseline (i. e., far away from ...Show More

Deep View Synthesis via Self-Consistent Generative Network

Zhuoman Liu;Wei Jia;Ming Yang;Peiyao Luo;Yong Guo;Mingkui Tan

Year: 2022 | Volume: 24 | Journal Article |

Improving Generative Adversarial Networks With Local Coordinate Coding

Jiezhang Cao;Yong Guo;Qingyao Wu;Chunhua Shen;Junzhou Huang;Mingkui Tan

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2022 | Volume: 44, Issue: 1 | Journal Article |
Cited by: Papers (10)
Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of real data and thus may lose semantic information (e.g., geometric structure or content in images) of data. In practice, the semantic information might be represented by some latent...Show More

Improving Generative Adversarial Networks With Local Coordinate Coding

Jiezhang Cao;Yong Guo;Qingyao Wu;Chunhua Shen;Junzhou Huang;Mingkui Tan

Year: 2022 | Volume: 44, Issue: 1 | Journal Article |

Contrastive Neural Architecture Search with Neural Architecture Comparators

Yaofo Chen;Yong Guo;Qi Chen;Minli Li;Wei Zeng;Yaowei Wang;Mingkui Tan

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (37)
One of the key steps in Neural Architecture Search (NAS) is to estimate the performance of candidate architectures. Existing methods either directly use the validation performance or learn a predictor to estimate the performance. However, these methods can be either computationally expensive or very inaccurate, which may severely affect the search efficiency and performance. Moreover, as it is ver...Show More

Contrastive Neural Architecture Search with Neural Architecture Comparators

Yaofo Chen;Yong Guo;Qi Chen;Minli Li;Wei Zeng;Yaowei Wang;Mingkui Tan

Year: 2021 | Conference Paper |

Conditional Automated Channel Pruning for Deep Neural Networks

Yixin Liu;Yong Guo;Jiaxin Guo;Luoqian Jiang;Jian Chen

IEEE Signal Processing Letters
Year: 2021 | Volume: 28 | Journal Article |
Cited by: Papers (12)
Channel pruning has become one of the predominant compression methods to deploy deep models on resource-constrained devices. Most channel pruning methods often use a fixed compression rate for all the layers of the model, which, however, may not be optimal. To address this issue, given a specific target compression rate, one can search for the optimal compression rate for each layer via some autom...Show More

Conditional Automated Channel Pruning for Deep Neural Networks

Yixin Liu;Yong Guo;Jiaxin Guo;Luoqian Jiang;Jian Chen

Year: 2021 | Volume: 28 | Journal Article |

Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution

Yong Guo;Jian Chen;Jingdong Wang;Qi Chen;Jiezhang Cao;Zeshuai Deng;Yanwu Xu;Mingkui Tan

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (261)
Deep neural networks have exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution (HR) images. However, there are two underlying limitations to existing SR methods. First, learning the mapping function from LR to HR images is typically an ill-posed problem, because there exist infinite HR images that...Show More

Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution

Yong Guo;Jian Chen;Jingdong Wang;Qi Chen;Jiezhang Cao;Zeshuai Deng;Yanwu Xu;Mingkui Tan

Year: 2020 | Conference Paper |

Hierarchical Neural Architecture Search for Single Image Super-Resolution

Yong Guo;Yongsheng Luo;Zhenhao He;Jin Huang;Jian Chen

IEEE Signal Processing Letters
Year: 2020 | Volume: 27 | Journal Article |
Cited by: Papers (35)
Deep neural networks have exhibited promising performance in image super-resolution (SR). Most SR models follow a hierarchical architecture that contains both the cell-level design of computational blocks and the network-level design of the positions of upsampling blocks. However, designing SR models heavily relies on human expertise and is very labor-intensive. More critically, these SR models of...Show More

Hierarchical Neural Architecture Search for Single Image Super-Resolution

Yong Guo;Yongsheng Luo;Zhenhao He;Jin Huang;Jian Chen

Year: 2020 | Volume: 27 | Journal Article |

Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis

Yong Guo;Qi Chen;Jian Chen;Qingyao Wu;Qinfeng Shi;Mingkui Tan

IEEE Transactions on Multimedia
Year: 2019 | Volume: 21, Issue: 11 | Journal Article |
Cited by: Papers (57)
Generating images via a generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce lowresolution images of limited quality. Directly generating highresolution images using GANs is nontrivial, and often produces problematic images with incomplete objects. To address this issue, we develop a novel GAN called auto-embed...Show More

Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis

Yong Guo;Qi Chen;Jian Chen;Qingyao Wu;Qinfeng Shi;Mingkui Tan

Year: 2019 | Volume: 21, Issue: 11 | Journal Article |

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