Author name cluster

Jinming Duan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

JBHI Journal 2025 Journal Article

TFNet: A Temporal-Frequency Domain Model for Gait Biomechanical Signal Prediction

Qingyao Bian
Weida Wang
Jinming Duan
Ziyun Ding

Precise prediction of upcoming gait signals, especially over an extended time scale like the entire gait cycle, is crucial. During this period, devices or gait retraining programs can respond to dynamic changes while considering multiple factors in the neurological and musculoskeletal systems. This enables effective adjustments, ultimately optimising outcomes based on the unique rehabilitation goals. However, current state-of-the-art models, whether driven by physical modelling or data modelling approaches, are constrained by short prediction time scales, limited accuracy, and high computational costs, which hinder their use on edge devices. We developed TFNet, a dual-stream neural network model that integrates temporal and frequency domain analyses to accurately predict biomechanical signals across the entire gait cycle. TFNet predicted lower limb joint angles and ground reaction forces with high precision, within 5 degrees and 0. 1 body weight, respectively. The model demonstrated the feasibility for deployment on edge devices and adaptability to patients with gait impairments. Explainability analysis highlighted key biomechanical features throughout the gait cycle, improving interpretability and clinical relevance. These comprehensive validations demonstrate the potential of TFNet as a reliable and cost-effective solution for clinical applications aimed at restoring and enhancing gait function.

Details DOI

AAAI Conference 2024 Conference Paper

Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems

Jintao Song
Wenqi Lu
Yunwen Lei
Yuchao Tang
Zhenkuan Pan
Jinming Duan

The Alternating Direction Method of Multipliers (ADMM) has gained significant attention across a broad spectrum of machine learning applications. Incorporating the over-relaxation technique shows potential for enhancing the convergence rate of ADMM. However, determining optimal algorithmic parameters, including both the associated penalty and relaxation parameters, often relies on empirical approaches tailored to specific problem domains and contextual scenarios. Incorrect parameter selection can significantly hinder ADMM's convergence rate. To address this challenge, in this paper we first propose a general approach to optimize the value of penalty parameter, followed by a novel closed-form formula to compute the optimal relaxation parameter in the context of linear quadratic problems (LQPs). We then experimentally validate our parameter selection methods through random instantiations and diverse imaging applications, encompassing diffeomorphic image registration, image deblurring, and MRI reconstruction.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Fourier-Net: Fast Image Registration with Band-Limited Deformation

Xi Jia
Joseph Bartlett
Wei Chen
Siyang Song
Tianyang Zhang
Xinxing Cheng
Wenqi Lu
Zhaowen Qiu

Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource-intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain. This representation is then decoded by our devised model-driven decoder (consisting of a zero padding layer and an inverse discrete Fourier transform layer) to the dense, full-resolution displacement field in the spatial domain. These changes allow our unsupervised Fourier-Net to contain fewer parameters and computational operations, resulting in faster inference speeds. Fourier-Net is then evaluated on two public 3D brain datasets against various state-of-the-art approaches. For example, when compared to a recent transformer-based method, named TransMorph, our Fourier-Net, which only uses 2.2% of its parameters and 6.66% of the multiply-add operations, achieves a 0.5% higher Dice score and an 11.48 times faster inference speed. Code is available at https://github.com/xi-jia/Fourier-Net.

PDF Details DOI

NeurIPS Conference 2023 Conference Paper

UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition

Qiufu Li
Xi Jia
Jiancan Zhou
Linlin Shen
Jinming Duan

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https: //github. com/CVI-SZU/UniTSFace.

PDF Details