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D. Caleb Rucker - IEEE Xplore Author Profile
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D. Caleb Rucker

Also published under: Daniel Caleb Rucker, Daniel Rücker

Publications
33
Citations
3,424
Publications by Year
20082025
Co-Authors:
Show All Co-Authors (62)
Image of author D. Caleb Rucker

D. Caleb Rucker

Also published under: Daniel Caleb Rucker, Daniel Rücker

Affiliation

Department of Mechanical Engineering
University of Tennessee at Knoxville
Knoxville, TN, USA

Publication Topics

Biography

D. Caleb Rucker (S’07–M’12) received the B.S. degree in engineering mechanics and mathematics from Lipscomb University, Nashville, TN, USA, in 2006, and the Ph.D. degree in mechanical engineering from Vanderbilt University, Nashville, TN, USA, in 2011, followed by postdoctoral research in biomedical engineering at Vanderbilt.,In 2013, he accepted a faculty position at the University of Tennessee, Knoxville, TN, USA. In 2007, he was an Engineer with Jacobs Technology Group, Tullahoma, TN, USA. Since 2012, he has been a Postdoctoral Research Associate at the Biomedical Modeling Laboratory (BML) at Vanderbilt University. His current research interests include medical robotics, continuum robotics, soft-tissue mechanics, control, and sensing.(Based on document published on 19 June 2013).
Publications
33
Citations
3,424
Publications by Year
20082025
Co-Authors:
Show All Co-Authors (62)
Author's Published Works

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Industrial Foundation Model

Lei Ren;Haiteng Wang;Jiabao Dong;Zidi Jia;Shixiang Li;Yuqing Wang;Yuanjun Laili;Di Huang;Lin Zhang;Bohu Li

IEEE Transactions on Cybernetics
Year: 2025 | Volume: 55, Issue: 5 | Journal Article |
Recently, foundation models (such as ChatGPT) have emerged with powerful learning, understanding, and generalization abilities, showcasing tremendous potential to revolutionarily promote modern industry. Despite significant advancements in various fields, existing general foundation models face challenges in industry when dealing with the data of specialized modalities, the tasks of varying-scenar...Show More

Industrial Foundation Model

Lei Ren;Haiteng Wang;Jiabao Dong;Zidi Jia;Shixiang Li;Yuqing Wang;Yuanjun Laili;Di Huang;Lin Zhang;Bohu Li

Year: 2025 | Volume: 55, Issue: 5 | Journal Article |

Generating Editable Head Avatars with 3D Gaussian GANs

Guohao Li;Hongyu Yang;Yifang Men;Di Huang;Weixin Li;Ruijie Yang;Yunhong Wang

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Generating animatable and editable 3D head avatars is essential for various applications in computer vision and graphics. Traditional 3D-aware generative adversarial networks (GANs), often using implicit fields like Neural Radiance Fields (NeRF), achieve photo-realistic and view-consistent 3D head synthesis. However, these methods face limitations in deformation flexibility and editability, hinder...Show More

Generating Editable Head Avatars with 3D Gaussian GANs

Guohao Li;Hongyu Yang;Yifang Men;Di Huang;Weixin Li;Ruijie Yang;Yunhong Wang

Year: 2025 | Conference Paper |

Joint Coarse to Fine-Grained Spatio-Temporal Modeling for Video Action Recognition

Chunlei Li;Can Cheng;Miao Yu;Zhoufeng Liu;Di Huang

IEEE Transactions on Biometrics, Behavior, and Identity Science
Year: 2025 | Early Access Article |
The action recognition task involves analyzing video content and temporal relationships between frames to identify actions. Crucial to this process are action representations that effectively capture varying temporal scales and spatial motion variations. To address these challenges, we propose the Joint Coarse to Fine-Grained Spatio-Temporal Modeling (JCFG-STM) approach, which is designed to captu...Show More

Joint Coarse to Fine-Grained Spatio-Temporal Modeling for Video Action Recognition

Chunlei Li;Can Cheng;Miao Yu;Zhoufeng Liu;Di Huang

Year: 2025 | Early Access Article |

Structure Representation With Adaptive and Compact Facial Graph for Micro-Expression Recognition

Chunlei Li;Renwei Ba;Xueping Wang;Miao Yu;Xiao Li;Di Huang

IEEE Transactions on Biometrics, Behavior, and Identity Science
Year: 2025 | Volume: 7, Issue: 2 | Journal Article |
The subtle and slight motions of micro-expressions (MEs) leave few effective features to micro-expression recognition (MER), making MER a challenging task. Existing works mainly focus on constructing strong representations from entire videos, individual frames, or redundant structural graphs, however, spatial structure feature learning of MEs leaves much space for further improvement. To solve the...Show More

Structure Representation With Adaptive and Compact Facial Graph for Micro-Expression Recognition

Chunlei Li;Renwei Ba;Xueping Wang;Miao Yu;Xiao Li;Di Huang

Year: 2025 | Volume: 7, Issue: 2 | Journal Article |

ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model With Implicit Neural Representations

Mingwu Zheng;Haiyu Zhang;Hongyu Yang;Liming Chen;Di Huang

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Volume: 47, Issue: 2 | Journal Article |
Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications. However, the challenges persist due to the limitations imposed by data discretization and model linearity, which hinder the precise capture of identity and expression clues in current studies. This paper presents a novel 3D morphable face model, named ImFace++, to learn a sophisti...Show More

ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model With Implicit Neural Representations

Mingwu Zheng;Haiyu Zhang;Hongyu Yang;Liming Chen;Di Huang

Year: 2025 | Volume: 47, Issue: 2 | Journal Article |

ALD-GCN: Graph Convolutional Networks With Attribute-Level Defense

Yihui Li;Yuanfang Guo;Junfu Wang;Shihao Nie;Liang Yang;Di Huang;Yunhong Wang

IEEE Transactions on Big Data
Year: 2025 | Volume: 11, Issue: 2 | Journal Article |
Graph Neural Networks(GNNs), such as Graph Convolutional Network, have exhibited impressive performance on various real-world datasets. However, many researches have confirmed that deliberately designed adversarial attacks can easily confuse GNNs on the classification of target nodes (targeted attacks) or all the nodes (global attacks). According to our observations, different attributes tend to b...Show More

ALD-GCN: Graph Convolutional Networks With Attribute-Level Defense

Yihui Li;Yuanfang Guo;Junfu Wang;Shihao Nie;Liang Yang;Di Huang;Yunhong Wang

Year: 2025 | Volume: 11, Issue: 2 | Journal Article |

A Micro-Expression Recognition Network Based on Attention Mechanism and Motion Magnification

Falin Wu;Yu Xia;Boyi Ma;Tianyang Hu;Jingyao Yang;Haoxin Li;Di Huang

IEEE Transactions on Affective Computing
Year: 2024 | Early Access Article |
Micro-expressions (MEs) are spontaneous facial movements that reveal an individual's genuine emotions and play a crucial role in various domains, including lie detection, criminal analysis, mental health treatment, national security, and others. Micro-expression recognition is a highly complex aspect within the domain of affective computing, aimed at identifying subtle facial motions that are diff...Show More

A Micro-Expression Recognition Network Based on Attention Mechanism and Motion Magnification

Falin Wu;Yu Xia;Boyi Ma;Tianyang Hu;Jingyao Yang;Haoxin Li;Di Huang

Year: 2024 | Early Access Article |

Progressive Self-supervised Representation Learning for 3D Facial Expression Recognition

Hebeizi Li;Hongyu Yang;Di Huang

2024 IEEE International Joint Conference on Biometrics (IJCB)
Year: 2024 | Conference Paper |
Facial expression recognition (FER) is a critical area of research in face analysis. While 2D data has been extensively used, 3D data offers inherent advantages, such as increased resilience to illumination and pose variations. However, the limited size of current 3D FER datasets significantly constrains the performance of 3D FER methods. To overcome this challenge, we propose a novel self-supervi...Show More

Progressive Self-supervised Representation Learning for 3D Facial Expression Recognition

Hebeizi Li;Hongyu Yang;Di Huang

Year: 2024 | Conference Paper |

Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge

Haoxiang Ma;Modi Shi;Boyang Gao;Di Huang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
We focus on the generalization ability of the 6-DoF grasp detection method in this paper. While learning-based grasp detection methods can predict grasp poses for unseen ob-jects using the grasp distribution learned from the training set, they often exhibit a significant performance drop when encountering objects with diverse shapes and struc-tures. To enhance the grasp detection methods' general-...Show More

Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge

Haoxiang Ma;Modi Shi;Boyang Gao;Di Huang

Year: 2024 | Conference Paper |

Initno: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

Xiefan Guo;Jinlin Liu;Miaomiao Cui;Jiankai Li;Hongyu Yang;Di Huang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Recent strides in the development of diffusion models, ex-emplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visu-ally compelling images. However, the imperative of achieving a seamless alignment between the generated image and the provided prompt persists as a formidable challenge. This paper traces the root of these difficulties to invalid...Show More

Initno: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

Xiefan Guo;Jinlin Liu;Miaomiao Cui;Jiankai Li;Hongyu Yang;Di Huang

Year: 2024 | Conference Paper |

Deep Common Feature Mining for Efficient Video Semantic Segmentation

Yaoyan Zheng;Hongyu Yang;Di Huang

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 12 | Journal Article |
Recent advancements in video semantic segmentation have made substantial progress by exploiting temporal correlations. Nevertheless, persistent challenges, including redundant computation and the reliability of the feature propagation process, underscore the need for further innovation. In response, we present Deep Common Feature Mining (DCFM), a novel approach strategically designed to address th...Show More

Deep Common Feature Mining for Efficient Video Semantic Segmentation

Yaoyan Zheng;Hongyu Yang;Di Huang

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

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

Haoxiang Ma;Ran Qin;Modi Shi;Boyang Gao;Di Huang

2024 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2024 | Conference Paper |
This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem. In this case, we present a global-to-local method to address hybrid domain gaps in RGB and depth data and insufficient multi-modal feature alignment. First, a self-supervised rotation pre-training strategy is adopted to deliver robust initialization for RGB and depth networks. We ...Show More

Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

Haoxiang Ma;Ran Qin;Modi Shi;Boyang Gao;Di Huang

Year: 2024 | Conference Paper |

OTAMatch: Optimal Transport Assignment With PseudoNCE for Semi-Supervised Learning

Jinjin Zhang;Junjie Liu;Debang Li;Qiuyu Huang;Jiaxin Chen;Di Huang

IEEE Transactions on Image Processing
Year: 2024 | Volume: 33 | Journal Article |
In semi-supervised learning (SSL), many approaches follow the effective self-training paradigm with consistency regularization, utilizing threshold heuristics to alleviate label noise. However, such threshold heuristics lead to the underutilization of crucial discriminative information from the excluded data. In this paper, we present OTAMatch, a novel SSL framework that reformulates pseudo-labeli...Show More

OTAMatch: Optimal Transport Assignment With PseudoNCE for Semi-Supervised Learning

Jinjin Zhang;Junjie Liu;Debang Li;Qiuyu Huang;Jiaxin Chen;Di Huang

Year: 2024 | Volume: 33 | Journal Article |

Towards Video Anomaly Detection in the Real World: A Binarization Embedded Weakly-Supervised Network

Zhen Yang;Yuanfang Guo;Junfu Wang;Di Huang;Xiuguo Bao;Yunhong Wang

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 5 | Journal Article |
Cited by: Papers (4)
In this letter, we pioneer to propose a binarization embedded weakly-supervised video anomaly detection (BE-WSVAD) method by constructing a binarized GCN-based anomaly detection module. Compared to the existing weakly-supervised video anomaly detection (WS-VAD) methods, BE-WSVAD focuses on the detection efficiency, which is ignored by the existing literature yet vital in real applications. Specifi...Show More

Towards Video Anomaly Detection in the Real World: A Binarization Embedded Weakly-Supervised Network

Zhen Yang;Yuanfang Guo;Junfu Wang;Di Huang;Xiuguo Bao;Yunhong Wang

Year: 2024 | Volume: 34, Issue: 5 | Journal Article |

Digital Twin Robotic System With Continuous Learning for Grasp Detection in Variable Scenes

Lei Ren;Jiabao Dong;Di Huang;Jinhu Lü

IEEE Transactions on Industrial Electronics
Year: 2024 | Volume: 71, Issue: 7 | Journal Article |
Cited by: Papers (5)
With the emergence of digitalization technology, digital twin bridges the gap between physical and virtual worlds in industrial production with synchronization, reliability, and fidelity. The manufacturing process of complex products needs multiple working procedures, where novel industrial parts occur, causing scenes to be variable for robots to perceive and grasp. Due to the geometric difference...Show More

Digital Twin Robotic System With Continuous Learning for Grasp Detection in Variable Scenes

Lei Ren;Jiabao Dong;Di Huang;Jinhu Lü

Year: 2024 | Volume: 71, Issue: 7 | Journal Article |

Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects

Lei Ren;Zidi Jia;Yuanjun Laili;Di Huang

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 11 | Journal Article |
Cited by: Papers (25)
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable intelligent process control, analysis, and management, such as complex equipment maintenance, product quality management, and dynamic process monitoring. Traditional methods face challenges in obtaining latent insights due to the growing complexity of IIoT. Recently, the latest development of deep lea...Show More

Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects

Lei Ren;Zidi Jia;Yuanjun Laili;Di Huang

Year: 2024 | Volume: 35, Issue: 11 | Journal Article |

FG-AGR: Fine-Grained Associative Graph Representation for Facial Expression Recognition in the Wild

Chunlei Li;Xiao Li;Xueping Wang;Di Huang;Zhoufeng Liu;Liang Liao

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 2 | Journal Article |
Cited by: Papers (19)
Facial expression recognition (FER) in the wild is challenging due to various unconstrained conditions, i.e., occlusions and head pose variations. Previous methods tend to improve the performance of facial expression recognition through resorting to holistic methods or coarse local-based methods, while ignoring the local fine-grained feature structure knowledge and the correlation between features...Show More

FG-AGR: Fine-Grained Associative Graph Representation for Facial Expression Recognition in the Wild

Chunlei Li;Xiao Li;Xueping Wang;Di Huang;Zhoufeng Liu;Liang Liao

Year: 2024 | Volume: 34, Issue: 2 | Journal Article |

DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration

Nan Zhou;Jiaxin Chen;Di Huang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The visual models pretrained on large-scale benchmarks encode general knowledge and prove effective in building more powerful representations for downstream tasks. Most existing approaches follow the fine-tuning paradigm, either by initializing or regularizing the downstream model based on the pretrained one. The former fails to retain the knowledge in the successive fine-tuning phase, thereby pro...Show More

DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration

Nan Zhou;Jiaxin Chen;Di Huang

Year: 2023 | Conference Paper |

Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection

Guodong Wang;Yunhong Wang;Jie Qin;Dongming Zhang;Xiuguo Bao;Di Huang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Anomaly detection (AD), aiming to find samples that deviate from the training distribution, is essential in safety-critical applications. Though recent self-supervised learning based attempts achieve promising results by creating virtual outliers, their training objectives are less faithful to AD which requires a concentrated inlier distribution as well as a dispersive outlier distribution. In thi...Show More

Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection

Guodong Wang;Yunhong Wang;Jie Qin;Dongming Zhang;Xiuguo Bao;Di Huang

Year: 2023 | Conference Paper |

Denoising Diffusion Autoencoders are Unified Self-supervised Learners

Weilai Xiang;Hongyu Yang;Di Huang;Yunhong Wang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (15)
Inspired by recent advances in diffusion models, which are reminiscent of denoising autoencoders, we investigate whether they can acquire discriminative representations for classification via generative pre-training. This paper shows that the networks in diffusion models, namely denoising diffusion autoencoders (DDAE), are unified self-supervised learners: by pre-training on unconditional image ge...Show More

Denoising Diffusion Autoencoders are Unified Self-supervised Learners

Weilai Xiang;Hongyu Yang;Di Huang;Yunhong Wang

Year: 2023 | Conference Paper |

OcTr: Octree-Based Transformer for 3D Object Detection

Chao Zhou;Yanan Zhang;Jiaxin Chen;Di Huang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (41)
A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering from inadequate receptive fields or coarse-grained holistic correlations. In this p...Show More

OcTr: Octree-Based Transformer for 3D Object Detection

Chao Zhou;Yanan Zhang;Jiaxin Chen;Di Huang

Year: 2023 | Conference Paper |

Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images

Bowei Du;Yecheng Huang;Jiaxin Chen;Di Huang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (68)
Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform. This paper investigates optimizing the detection head based on the sparse convolution, which proves effective in balancing the accuracy and efficiency. Nevertheless, it suffers from inadequate integration of contextual information of tiny object...Show More

Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images

Bowei Du;Yecheng Huang;Jiaxin Chen;Di Huang

Year: 2023 | Conference Paper |

NeuFace: Realistic 3D Neural Face Rendering from Multi-View Images

Mingwu Zheng;Haiyu Zhang;Hongyu Yang;Di Huang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to complex spatially-varying reflectance properties and geometry characteristics of faces, however, it remains challenging to recover 3D facial representations both faithfully and efficiently in the current studies. This paper presents a novel 3D face rendering model, namely NeuF...Show More

NeuFace: Realistic 3D Neural Face Rendering from Multi-View Images

Mingwu Zheng;Haiyu Zhang;Hongyu Yang;Di Huang

Year: 2023 | Conference Paper |

RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention

Ran Qin;Haoxiang Ma;Boyang Gao;Di Huang

2023 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2023 | Conference Paper |
Cited by: Papers (14)
Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities. However, depth maps are generally of a relatively lower quality with much stronger noise compared to RGB images, making it challenging to acquire grasp depth and fuse m...Show More

RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention

Ran Qin;Haoxiang Ma;Boyang Gao;Di Huang

Year: 2023 | Conference Paper |

Key Role Guided Transformer for Group Activity Recognition

Duoxuan Pei;Di Huang;Longteng Kong;Yunhong Wang

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2023 | Volume: 33, Issue: 12 | Journal Article |
Cited by: Papers (8)
Group Activity Recognition (GAR) is a challenging task, where modeling spatio-temporal relationships among participants plays a fundamental role. To address this issue, we propose a novel end-to-end trainable network, termed Key Role Guided Transformer (KRGFormer). Different from current methods that concurrently take all individuals into account for global reasoning, it captures crucial contextua...Show More

Key Role Guided Transformer for Group Activity Recognition

Duoxuan Pei;Di Huang;Longteng Kong;Yunhong Wang

Year: 2023 | Volume: 33, Issue: 12 | Journal Article |

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