Michael R. Turski - IEEE Xplore Author Profile

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Author details

Michael R. Turski

Publications

1

Citations

2

Publications by Year

20232023

Co-Authors:

Aaron M. JohnsonJoseph Norby

Michael R. Turski

Affiliation

Department of Mechanical Engineering

Carnegie Mellon University

Pittsburgh, PA, USA

Publication Topics

3rd Order,
Collocation Method,
Complicated Task,
Contact Force,
Contact Point,
Control Limits,
Cost Function,
Decision Variables,
End Conditions,
Finite Element,
Finite Number Of Elements,
Flight Phase

Publications

1

Citations

2

Publications by Year

20232023

Co-Authors:

Aaron M. Johnson
Joseph Norby

Author's Published Works

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Showing 1-20 of 20 results

Journals (11)

Conferences (7)

Early Access Articles (2)

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Huaidong Zhang(20)
Xuemiao Xu(19)
Shengfeng He(13)
Bangzhen Liu(4)
Cheng Xu(4)
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China(7)
State Key Laboratory of Subtropical Building Science(5)
Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information(5)
South China University of Technology(5)
School of Future Technology, South China University of Technology, Guangzhou, China(5)
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(3)
IEEE Transactions on Circuits and Systems for Video Technology(3)
IEEE Transactions on Visualization and Computer Graphics(3)
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2)
IEEE Transactions on Image Processing(2)
IEEE(20)
Media(4)
Seattle, WA, USA(4)
Vancouver, BC, Canada(2)
Yokohama, Japan(1)
Generative Adversarial Networks(5)
Perceptual Loss(5)
Image Synthesis(4)
Latent Space(4)
Conduct Ablation Experiments(3)

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Recurrent Diffusion for 3D Point Cloud Generation From a Single Image

Yan Zhou;Dewang Ye;Huaidong Zhang;Xuemiao Xu;Huajie Sun;Yewen Xu;Xiangyu Liu;Yuexia Zhou

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

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Single-image 3D shape reconstruction has attracted significant attention with the advance of generative models. Recent studies have utilized diffusion models to achieve unprecedented shape reconstruction quality. However, these methods, in each sampling step, perform denoising in a single forward pass, leading to cumulative errors that severely impact the geometric consistency of the generated sha...Show More

Recurrent Diffusion for 3D Point Cloud Generation From a Single Image

Yan Zhou;Dewang Ye;Huaidong Zhang;Xuemiao Xu;Huajie Sun;Yewen Xu;Xiangyu Liu;Yuexia Zhou

IEEE Transactions on Image Processing

Year: 2025 | Volume: 34 | Journal Article |

Rotation-Adaptive Point Cloud Domain Generalization Via Intricate Orientation Learning

Bangzhen Liu;Chenxi Zheng;Xuemiao Xu;Cheng Xu;Huaidong Zhang;Shengfeng He

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Early Access Article |

The vulnerability of 3D point cloud analysis to unpredictable rotations poses an open yet challenging problem: orientation-aware 3D domain generalization. Cross-domain robustness and adaptability of 3D representations are crucial but not easily achieved through rotation augmentation. Motivated by the inherent advantages of intricate orientations in enhancing generalizability, we propose an innovat...Show More

Rotation-Adaptive Point Cloud Domain Generalization Via Intricate Orientation Learning

Bangzhen Liu;Chenxi Zheng;Xuemiao Xu;Cheng Xu;Huaidong Zhang;Shengfeng He

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2025 | Early Access Article |

Learning an Interpretable Stylized Subspace for 3D-Aware Animatable Artforms

Chenxi Zheng;Bangzhen Liu;Xuemiao Xu;Huaidong Zhang;Shengfeng He

IEEE Transactions on Visualization and Computer Graphics

Year: 2025 | Volume: 31, Issue: 2 | Journal Article |

Cited by: Papers (2)

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Throughout history, static paintings have captivated viewers within display frames, yet the possibility of making these masterpieces vividly interactive remains intriguing. This research paper introduces 3DArtmator, a novel approach that aims to represent artforms in a highly interpretable stylized space, enabling 3D-aware animatable reconstruction and editing. Our rationale is to transfer the int...Show More

Learning an Interpretable Stylized Subspace for 3D-Aware Animatable Artforms

Chenxi Zheng;Bangzhen Liu;Xuemiao Xu;Huaidong Zhang;Shengfeng He

IEEE Transactions on Visualization and Computer Graphics

Year: 2025 | Volume: 31, Issue: 2 | Journal Article |

Mask4Align: Aligned Entity Prompting with Color Masks for Multi-Entity Localization Problems

Haoquan Zhang;Ronggang Huang;Yi Xie;Huaidong Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

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In Visual Question Answering (VQA), recognizing and localizing entities pose significant challenges. Pretrained vision-and-language models have addressed this problem by providing a text description as the answer. However, in visual scenes with multiple entities, textual descriptions struggle to distinguish the entities from the same category effectively. Consequently, the VQA dataset is limited b...Show More

Mask4Align: Aligned Entity Prompting with Color Masks for Multi-Entity Localization Problems

Haoquan Zhang;Ronggang Huang;Yi Xie;Huaidong Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

D3still: Decoupled Differential Distillation for Asymmetric Image Retrieval

Yi Xie;Yihong Lin;Wenjie Cai;Xuemiao Xu;Huaidong Zhang;Yong Du;Shengfeng He

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Cited by: Papers (3)

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Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However, these one-to-one constraint approaches often fail to maintain retrieval order consistency, especially when the query network has limited representational capacity. To overcome this problem, we introduce the Decoupled Differential Distillat...Show More

D3still: Decoupled Differential Distillation for Asymmetric Image Retrieval

Yi Xie;Yihong Lin;Wenjie Cai;Xuemiao Xu;Huaidong Zhang;Yong Du;Shengfeng He

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer Examples

Yuyang Yu;Bangzhen Liu;Chenxi Zheng;Xuemiao Xu;Shengfeng He;Huaidong Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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In this paper, we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end, we implement two optimization strategies. The first, prompt-free conditional learning, utilizes a prompt-free encoder derived from a pre-trained ...Show More

Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer Examples

Yuyang Yu;Bangzhen Liu;Chenxi Zheng;Xuemiao Xu;Shengfeng He;Huaidong Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Exploiting Multi-View Clues for Context-Aware Unified Lumbar MRI Identification and Diagnosis

Weiwen Zhang;Cheng Xu;Xuemiao Xu;Huaidong Zhang;Rongchen Zhao;Jing Qin

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

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Lumbar disc herniation, as one of the most common spinal degeneration diseases, significantly affects the quality of people’s lives. Effective identification and diagnosis of this disease is highly demanded and crucial to improve lumbar disc health care. In this paper, we propose a unified framework for diagnosing multiple lumbar degeneration diseases in MRI. Considering the basis of diagnosis is ...Show More

Exploiting Multi-View Clues for Context-Aware Unified Lumbar MRI Identification and Diagnosis

Weiwen Zhang;Cheng Xu;Xuemiao Xu;Huaidong Zhang;Rongchen Zhao;Jing Qin

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

G²Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

Haoxin Yang;Xuemiao Xu;Cheng Xu;Huaidong Zhang;Jing Qin;Yi Wang;Pheng-Ann Heng;Shengfeng He

IEEE Transactions on Information Forensics and Security

Year: 2024 | Volume: 19 | Journal Article |

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Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent man...Show More

G²Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

Haoxin Yang;Xuemiao Xu;Cheng Xu;Huaidong Zhang;Jing Qin;Yi Wang;Pheng-Ann Heng;Shengfeng He

IEEE Transactions on Information Forensics and Security

Year: 2024 | Volume: 19 | Journal Article |

Delving Into Important Samples of Semi-Supervised Old Photo Restoration: A New Dataset and Method

Weiwei Cai;Huaidong Zhang;Xuemiao Xu;Chenshu Xu;Kun Zhang;Shengfeng He

IEEE Transactions on Multimedia

Year: 2024 | Volume: 26 | Journal Article |

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The degradation of printed photographs due to inadequate preservation is a major problem that can be addressed through deep learning-based restoration methods. However, these methods are often limited by their reliance on annotated data, making them less effective for new domains with limited training samples. In this paper, we propose a semi-supervised old photo restoration network that employs a...Show More

Delving Into Important Samples of Semi-Supervised Old Photo Restoration: A New Dataset and Method

Weiwei Cai;Huaidong Zhang;Xuemiao Xu;Chenshu Xu;Kun Zhang;Shengfeng He

IEEE Transactions on Multimedia

Year: 2024 | Volume: 26 | Journal Article |

DreamAnime: Learning Style-Identity Textual Disentanglement for Anime and Beyond

Chenshu Xu;Yangyang Xu;Huaidong Zhang;Xuemiao Xu;Shengfeng He

IEEE Transactions on Visualization and Computer Graphics

Year: 2024 | Early Access Article |

Text-to-image generation models have significantly broadened the horizons of creative expression through the power of natural language. However, navigating these models to generate unique concepts, alter their appearance, or reimagine them in unfamiliar roles presents an intricate challenge. For instance, how can we exploit language-guided models to transpose an anime character into a different ar...Show More

DreamAnime: Learning Style-Identity Textual Disentanglement for Anime and Beyond

Chenshu Xu;Yangyang Xu;Huaidong Zhang;Xuemiao Xu;Shengfeng He

IEEE Transactions on Visualization and Computer Graphics

Year: 2024 | Early Access Article |

Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval

Yi Xie;Huaidong Zhang;Xuemiao Xu;Jianqing Zhu;Shengfeng He

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (14)

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Previous Knowledge Distillation based efficient image retrieval methods employ a lightweight network as the stu-dent model for fast inference. However, the lightweight stu-dent model lacks adequate representation capacity for effective knowledge imitation during the most critical early training period, causing final performance degeneration. To tackle this issue, we propose a Capacity Dynamic Dist...Show More

Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval

Yi Xie;Huaidong Zhang;Xuemiao Xu;Jianqing Zhu;Shengfeng He

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Where is My Spot? Few-shot Image Generation via Latent Subspace Optimization

Chenxi Zheng;Bangzhen Liu;Huaidong Zhang;Xuemiao Xu;Shengfeng He

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (10)

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Image generation relies on massive training data that can hardly produce diverse images of an unseen category according to a few examples. In this paper, we address this dilemma by projecting sparse few-shot samples into a continuous latent space that can potentially generate infinite unseen samples. The rationale behind is that we aim to locate a centroid latent position in a conditional StyleGAN...Show More

Where is My Spot? Few-shot Image Generation via Latent Subspace Optimization

Chenxi Zheng;Bangzhen Liu;Huaidong Zhang;Xuemiao Xu;Shengfeng He

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Panel-Page-Aware Comic Genre Understanding

Chenshu Xu;Xuemiao Xu;Nanxuan Zhao;Weiwei Cai;Huaidong Zhang;Chengze Li;Xueting Liu

IEEE Transactions on Image Processing

Year: 2023 | Volume: 32 | Journal Article |

Cited by: Papers (3)

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Using a sequence of discrete still images to tell a story or introduce a process has become a tradition in the field of digital visual media. With the surge in these media and the requirements in downstream tasks, acquiring their main topics or genres in a very short time is urgently needed. As a representative form of the media, comic enjoys a huge boom as it has gone digital. However, different ...Show More

Panel-Page-Aware Comic Genre Understanding

Chenshu Xu;Xuemiao Xu;Nanxuan Zhao;Weiwei Cai;Huaidong Zhang;Chengze Li;Xueting Liu

IEEE Transactions on Image Processing

Year: 2023 | Volume: 32 | Journal Article |

Contextual-Assisted Scratched Photo Restoration

Weiwei Cai;Huaidong Zhang;Xuemiao Xu;Shengfeng He;Kun Zhang;Jing Qin

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2023 | Volume: 33, Issue: 10 | Journal Article |

Cited by: Papers (7)

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Printed photographs can be easily warped, wrinkled, and even deteriorated over time. Existing methods treat the restoration of scratches as a pure inpainting problem that neglects the underlying corrupted contextual knowledge. However, important underlying contents are hidden behind the scratches, which are essential hints for producing a semantically consistent result. Motivated by this insight, ...Show More

Contextual-Assisted Scratched Photo Restoration

Weiwei Cai;Huaidong Zhang;Xuemiao Xu;Shengfeng He;Kun Zhang;Jing Qin

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2023 | Volume: 33, Issue: 10 | Journal Article |

Representative Feature Alignment for Adaptive Object Detection

Shan Xu;Huaidong Zhang;Xuemiao Xu;Xiaowei Hu;Yangyang Xu;Liangui Dai;Kup-Sze Choi;Pheng-Ann Heng

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2023 | Volume: 33, Issue: 2 | Journal Article |

Cited by: Papers (9)

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Unsupervised domain adaptation for object detection aims to generalize the object detector trained on the label-rich source domain to the unlabeled target domain. Recently, existing works adopt the instance-level alignment or pixel-level alignment to perform domain transfer, which can effectively avoid the negative transfer due to the diverse background between domains. However, we find that they ...Show More

Representative Feature Alignment for Adaptive Object Detection

Shan Xu;Huaidong Zhang;Xuemiao Xu;Xiaowei Hu;Yangyang Xu;Liangui Dai;Kup-Sze Choi;Pheng-Ann Heng

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2023 | Volume: 33, Issue: 2 | Journal Article |

Adaptive Selecting and Learning Network and a New Benchmark for Imbalanced Fine-Grained Ship Classification

Yujie Xu;Minghao Yan;Cheng Xu;Huaidong Zhang;Yijun Liu;Xuemiao Xu

Year: 2021 | Volume: 9 | Journal Article |

Cited by: Papers (4)

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Ship classification is one of the most essential tasks in ship surveillance, which is an important but challenging problem. Most existing methods are designed for remote sensing images and there are few works processing natural ship images captured by camera. In this paper, we design a new framework called Adaptive Selecting and Learning Network (ASL), to solve the problem of fine-grained classifi...Show More

Adaptive Selecting and Learning Network and a New Benchmark for Imbalanced Fine-Grained Ship Classification

Yujie Xu;Minghao Yan;Cheng Xu;Huaidong Zhang;Yijun Liu;Xuemiao Xu

Year: 2021 | Volume: 9 | Journal Article |

Perceptual-Aware Sketch Simplification Based on Integrated VGG Layers

Xuemiao Xu;Minshan Xie;Peiqi Miao;Wei Qu;Wenpeng Xiao;Huaidong Zhang;Xueting Liu;Tien-Tsin Wong

IEEE Transactions on Visualization and Computer Graphics

Year: 2021 | Volume: 27, Issue: 1 | Journal Article |

Cited by: Papers (29)

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Deep learning has been recently demonstrated as an effective tool for raster-based sketch simplification. Nevertheless, it remains challenging to simplify extremely rough sketches. We found that a simplification network trained with a simple loss, such as pixel loss or discriminator loss, may fail to retain the semantically meaningful details when simplifying a very sketchy and complicated drawing...Show More

Perceptual-Aware Sketch Simplification Based on Integrated VGG Layers

Xuemiao Xu;Minshan Xie;Peiqi Miao;Wei Qu;Wenpeng Xiao;Huaidong Zhang;Xueting Liu;Tien-Tsin Wong

IEEE Transactions on Visualization and Computer Graphics

Year: 2021 | Volume: 27, Issue: 1 | Journal Article |

Context-Aware and Scale-Insensitive Temporal Repetition Counting

Huaidong Zhang;Xuemiao Xu;Guoqiang Han;Shengfeng He

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Cited by: Papers (26)

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Temporal repetition counting aims to estimate the number of cycles of a given repetitive action. Existing deep learning methods assume repetitive actions are performed in a fixed time-scale, which is invalid for the complex repetitive actions in real life. In this paper, we tailor a context-aware and scale-insensitive framework, to tackle the challenges in repetition counting caused by the unknown...Show More

Context-Aware and Scale-Insensitive Temporal Repetition Counting

Huaidong Zhang;Xuemiao Xu;Guoqiang Han;Shengfeng He

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2020 | Conference Paper |

Unsupervised Domain Adaptation via Importance Sampling

Xuemiao Xu;Hai He;Huaidong Zhang;Yangyang Xu;Shengfeng He

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2020 | Volume: 30, Issue: 12 | Journal Article |

Cited by: Papers (39)

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Unsupervised domain adaptation aims to generalize a model from the label-rich source domain to the unlabeled target domain. Existing works mainly focus on aligning the global distribution statistics between source and target domains. However, they neglect distractions from the unexpected noisy samples in domain distribution estimation, leading to domain misalignment or even negative transfer. In t...Show More

Unsupervised Domain Adaptation via Importance Sampling

Xuemiao Xu;Hai He;Huaidong Zhang;Yangyang Xu;Shengfeng He

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2020 | Volume: 30, Issue: 12 | Journal Article |

Fast User-Guided Single Image Reflection Removal via Edge-Aware Cascaded Networks

Huaidong Zhang;Xuemiao Xu;Hai He;Shengfeng He;Guoqiang Han;Jing Qin;Dapeng Wu

IEEE Transactions on Multimedia

Year: 2020 | Volume: 22, Issue: 8 | Journal Article |

Cited by: Papers (15)

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Taking photos through a glass window leads to glare or reflection, which might distract the viewer from the scene behind the window. In this paper, we involve user interaction to tackle the ill-posedness of the reflection removal problem. Users are allowed to draw strokes or lassos to indicate the background and reflection layers. Instead of designing hand-crafted features, we propose the edge-awa...Show More

Fast User-Guided Single Image Reflection Removal via Edge-Aware Cascaded Networks

Huaidong Zhang;Xuemiao Xu;Hai He;Shengfeng He;Guoqiang Han;Jing Qin;Dapeng Wu

IEEE Transactions on Multimedia

Year: 2020 | Volume: 22, Issue: 8 | Journal Article |

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