Kaichen Zhou - IEEE Xplore Author Profile
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Kaichen Zhou

Publications
12
Citations
56
Publications by Year
20222024
Co-Authors:
Show All Co-Authors (32)
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Kaichen Zhou

Affiliation

Department of Computer Science, University of Oxford

Publication Topics

Biography

Kaichen Zhou (Student Member, IEEE) received the master’s degree in machine learning from the Department of Computing, Imperial College London, London, U.K. He is currently pursuing the Ph.D. degree in computer science with the Department of Computer Science, University of Oxford, Oxford, U.K.,His research interests include deep learning and reinforcement learning.(Based on document published on 20 July 2020).
Publications
12
Citations
56
Publications by Year
20222024
Co-Authors:
Show All Co-Authors (32)
Author's Published Works

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Learning Generalizable Manipulation Policy with Adapter-Based Parameter Fine-Tuning

Kai Lu;Kim Tien Ly;William Hebberd;Kaichen Zhou;Ioannis Havoutis;Andrew Markham

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
This study investigates the use of adapters in reinforcement learning for robotic skill generalization across multiple robots and tasks. Traditional methods are typically reliant on robot-specific retraining and face challenges such as efficiency and adaptability, particularly when scaling to robots with varying kinematics. We propose an alternative approach where a disembodied (virtual) hand mani...Show More

Learning Generalizable Manipulation Policy with Adapter-Based Parameter Fine-Tuning

Kai Lu;Kim Tien Ly;William Hebberd;Kaichen Zhou;Ioannis Havoutis;Andrew Markham

Year: 2024 | Conference Paper |

WSCLoc: Weakly-Supervised Sparse-View Camera Relocalization via Radiance Field

Jialu Wang;Kaichen Zhou;Andrew Markham;Niki Trigoni

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
Despite the advancements in deep learning for camera relocalization tasks, obtaining ground truth pose labels required for the training process remains a costly endeavor. While current weakly supervised methods excel in lightweight label generation, their performance notably declines in scenarios with sparse views. In response to this challenge, we introduce WSCLoc, a system capable of being custo...Show More

WSCLoc: Weakly-Supervised Sparse-View Camera Relocalization via Radiance Field

Jialu Wang;Kaichen Zhou;Andrew Markham;Niki Trigoni

Year: 2024 | Conference Paper |

SCANet: Correcting LEGO Assembly Errors with Self-Correct Assembly Network

Yuxuan Wan;Kaichen Zhou;Jinhong Chen;Hao Dong

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Autonomous assembly in robotics and 3D vision presents significant challenges, particularly in ensuring assembly correctness. Presently, predominant methods such as MEPNet focus on assembling components based on manually provided images. However, these approaches often fall short in achieving satisfactory results for tasks requiring long-term planning. Concurrently, we observe that integrating a s...Show More

SCANet: Correcting LEGO Assembly Errors with Self-Correct Assembly Network

Yuxuan Wan;Kaichen Zhou;Jinhong Chen;Hao Dong

Year: 2024 | Conference Paper |

Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation

Sangyun Shin;Kaichen Zhou;Madhu Vankadari;Andrew Markham;Niki Trigoni

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based, Kernel-based and Transformer-based methods. We argue that this is due to two limitations: 1) Instance size over-estimation by axis-aligned bounding box(AABB) 2) False negative error accumulation from inaccurate box to the re-finement phase. In this work, we introduce Spherical Mask, a novel co...Show More

Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation

Sangyun Shin;Kaichen Zhou;Madhu Vankadari;Andrew Markham;Niki Trigoni

Year: 2024 | Conference Paper |

Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models

Madhu Vankadari;Samuel Hodgson;Sangyun Shin;Kaichen Zhou;Andrew Markham;Niki Trigoni

2024 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Self-supervised depth estimation algorithms rely heavily on frame-warping relationships, exhibiting substantial performance degradation when applied in challenging circumstances, such as low-visibility and nighttime scenarios with varying illumination conditions. Addressing this challenge, we introduce an algorithm designed to achieve accurate selfsupervised stereo depth estimation focusing on nig...Show More

Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models

Madhu Vankadari;Samuel Hodgson;Sangyun Shin;Kaichen Zhou;Andrew Markham;Niki Trigoni

Year: 2024 | Conference Paper |

OV-NeRF: Open-Vocabulary Neural Radiance Fields With Vision and Language Foundation Models for 3D Semantic Understanding

Guibiao Liao;Kaichen Zhou;Zhenyu Bao;Kanglin Liu;Qing Li

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 12 | Journal Article |
Cited by: Papers (2)
The development of Neural Radiance Fields (NeRFs) has provided a potent representation for encapsulating the geometric and appearance characteristics of 3D scenes. Enhancing the capabilities of NeRFs in open-vocabulary 3D semantic perception tasks has been a recent focus. However, current methods that extract semantics directly from Contrastive Language-Image Pretraining (CLIP) for semantic field ...Show More

OV-NeRF: Open-Vocabulary Neural Radiance Fields With Vision and Language Foundation Models for 3D Semantic Understanding

Guibiao Liao;Kaichen Zhou;Zhenyu Bao;Kanglin Liu;Qing Li

Year: 2024 | Volume: 34, Issue: 12 | Journal Article |

RGBGrasp: Image-Based Object Grasping by Capturing Multiple Views During Robot arm Movement With Neural Radiance Fields

Chang Liu;Kejian Shi;Kaichen Zhou;Haoxiao Wang;Jiyao Zhang;Hao Dong

IEEE Robotics and Automation Letters
Year: 2024 | Volume: 9, Issue: 6 | Journal Article |
Cited by: Papers (4)
Robotic research encounters a significant hurdle when it comes to the intricate task of grasping objects that come in various shapes, materials, and textures. Unlike many prior investigations that heavily leaned on specialized point-cloud cameras or abundant RGB visual data to gather 3D insights for object-grasping missions, this letter introduces a pioneering approach called RGBGrasp. This method...Show More

RGBGrasp: Image-Based Object Grasping by Capturing Multiple Views During Robot arm Movement With Neural Radiance Fields

Chang Liu;Kejian Shi;Kaichen Zhou;Haoxiao Wang;Jiyao Zhang;Hao Dong

Year: 2024 | Volume: 9, Issue: 6 | Journal Article |

Beyond Fusion: Modality Hallucination-based Multispectral Fusion for Pedestrian Detection

Qian Xie;Ta-Ying Cheng;Jia-Xing Zhong;Kaichen Zhou;Andrew Markham;Niki Trigoni

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Pedestrian detection is a fundamental task for many downstream applications. Visible and thermal images, as the two most important data types, are usually used to detect pedestrians under various environmental conditions. Many state-of-the-art works have been proposed to use two-stream (i.e., two-branch) architectures to combine visible and thermal information to improve detection performance. How...Show More

Beyond Fusion: Modality Hallucination-based Multispectral Fusion for Pedestrian Detection

Qian Xie;Ta-Ying Cheng;Jia-Xing Zhong;Kaichen Zhou;Andrew Markham;Niki Trigoni

Year: 2024 | Conference Paper |

SSL-Net: A Synergistic Spectral and Learning-Based Network for Efficient Bird Sound Classification

Yiyuan Yang;Kaichen Zhou;Niki Trigoni;Andrew Markham

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Efficient and accurate bird sound classification is of important for ecology, habitat protection and scientific research, as it plays a central role in monitoring the distribution and abundance of species. However, prevailing methods typically demand extensively labeled audio datasets and have highly customized frameworks, imposing substantial computational and annotation loads. In this study, we ...Show More

SSL-Net: A Synergistic Spectral and Learning-Based Network for Efficient Bird Sound Classification

Yiyuan Yang;Kaichen Zhou;Niki Trigoni;Andrew Markham

Year: 2024 | Conference Paper |

Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR

Sangyun Shin;Stuart Golodetz;Madhu Vankadari;Kaichen Zhou;Andrew Markham;Niki Trigoni

2023 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2023 | Conference Paper |
Deep learning has led to great progress in the detection of mobile (i.e. movement-capable) objects in urban driving scenes in recent years. Supervised approaches typically require the annotation of large training sets; there has thus been great interest in leveraging weakly, semi- or self- supervised methods to avoid this, with much success. Whilst weakly and semi-supervised methods require some a...Show More

Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR

Sangyun Shin;Stuart Golodetz;Madhu Vankadari;Kaichen Zhou;Andrew Markham;Niki Trigoni

Year: 2023 | Conference Paper |

No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces

Jia-Xing Zhong;Kaichen Zhou;Qingyong Hu;Bing Wang;Niki Trigoni;Andrew Markham

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (14)
Scene flow is a powerful tool for capturing the motion field of 3D point clouds. However, it is difficult to directly apply flow-based models to dynamic point cloud classification since the unstructured points make it hard or even impossible to efficiently and effectively trace point-wise correspondences. To capture 3D motions without explicitly tracking correspondences, we propose a kinematics-in...Show More

No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces

Jia-Xing Zhong;Kaichen Zhou;Qingyong Hu;Bing Wang;Niki Trigoni;Andrew Markham

Year: 2022 | Conference Paper |

Smart Train Operation Algorithms Based on Expert Knowledge and Reinforcement Learning

Kaichen Zhou;Shiji Song;Anke Xue;Keyou You;Hui Wu

IEEE Transactions on Systems, Man, and Cybernetics: Systems
Year: 2022 | Volume: 52, Issue: 2 | Journal Article |
Cited by: Papers (29)
During decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems for its low-cost and intelligence. This article proposes two smart train operation (STO) algorithms by integrating the expert knowledge with reinforcement learning algorithms. Compared with previous works, the proposed algorithms can realize the control of continuous action for the subway s...Show More

Smart Train Operation Algorithms Based on Expert Knowledge and Reinforcement Learning

Kaichen Zhou;Shiji Song;Anke Xue;Keyou You;Hui Wu

Year: 2022 | Volume: 52, Issue: 2 | Journal Article |

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