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Yipeng Liu

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5 papers

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EAAI Journal 2025 Journal Article

A non-destructive automatic pavement damage detection scheme based on end-to-end neural networks with multi-level attention mechanism

Yipeng Liu
Chuan Wang
Yingchao Zhang
Xiteng Sun
Cong Du
Dongdong Xie
Yuan Tian

The accurate classification and statistics of road damage detection technology are crucial for road condition evaluation and maintenance decisions. However, the accuracy of complex road surface damage detection based on deep learning is still insufficient for real engineering, and even one of the damages may be repeatedly counted. This study develops a new non-destructive automatic road damage detection technology that includes detect road damage based on deep learning and redundant damage image de-duplication. This technology based on multi-level attention mechanism is designed from the perspectives of convolutional kernels and loss functions, improves the accuracy of real road surface damage detection. Compared to the original network, mAP@0. 5 and F1 score increase by 5. 1 % and 4 % for the public dataset RDD-2020, respectively. This technology achieves de-duplicate accuracy of 94. 29 % in the duplicate road damage dataset (DRDD) by adding image processing algorithm, which will accelerate the engineering application of non-destructive automatic pavement damage detection.

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YNIMG Journal 2025 Journal Article

DeepNuParc: A novel deep clustering framework for fine-scale parcellation of brain nuclei using diffusion MRI tractography

Haolin He
Ce Zhu
Le Zhang
Yipeng Liu
Xiao Xu
Yuqian Chen
Leo Zekelman
Jarrett Rushmore

Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation of the brain nuclei is vital for a comprehensive understanding of their anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the nuclei of interest for studying their subdivisions. In this work, we present a deep clustering pipeline, namely DeepNuParc, to perform automated, fine-scale parcellation of brain nuclei using diffusion MRI tractography. First, we incorporate a newly proposed deep learning approach to enable accurate segmentation of the nuclei of interest directly on the dMRI data. Next, we design a novel streamline clustering-based structural connectivity feature for a robust representation of voxels within the nuclei. Finally, we improve the popular joint dimensionality reduction and k-means clustering approach to enable nuclei parcellation at a finer scale. We demonstrate DeepNuParc on two important brain structures, i.e. the amygdala and the thalamus, that are known to have multiple anatomically and functionally distinct nucleus subdivisions. Experimental results show that DeepNuParc enables consistent parcellation of the nuclei into multiple parcels across multiple subjects and achieves good correspondence with the widely used coarse-scale atlases. Our code is available at https://github.com/HarlandZZC/deep_nuclei_parcellation.

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AAAI Conference 2025 Conference Paper

SLR-MVTC: Smooth Low-Rank Multi-View Tensor Clustering

Zhen Long
Yipeng Liu
Yazhou Ren
Ce Zhu

Multi-view tensor clustering (MVTC) has gained much attention for its effectiveness in capturing global high-order correlations across views. However, current MVTC methods suffer from two limitations: 1) adopting a two-stage process to learn the latent features for clustering, and 2) either ignoring local similarities within views or treating local similarities and global high-order correlations equally. In this paper, we propose a smooth low-rank MVTC (SLR-MVTC) method, which aims to extract latent features that are smooth within each view and low-rank across views, enhancing clustering performance. Specifically, we first learn latent features from each view using orthogonal projection and then construct the latent feature tensor by concatenation and rotation. Then, we introduce a new smooth tensor nuclear norm to depict the low-rank components of the low-frequency parts in the feature tensor. Benefiting from the fast Fourier transform along the sample dimension, the obtained low-frequency components effectively capture local smoothness within views, while their low-rank parts further explore global correlations across views. Experimental results on six multi-view datasets demonstrate that SLR-MVTC outperforms state-of-the-art algorithms in terms of clustering performance and CPU time.

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NeurIPS Conference 2024 Conference Paper

Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization

Ziyu Shan
Yujie Zhang
Yipeng Liu
Yiling Xu

No-Reference Point Cloud Quality Assessment (NR-PCQA) aims to objectively assess the human perceptual quality of point clouds without relying on pristine-quality point clouds for reference. It is becoming increasingly significant with the rapid advancement of immersive media applications such as virtual reality (VR) and augmented reality (AR). However, current NR-PCQA models attempt to indiscriminately learn point cloud content and distortion representations within a single network, overlooking their distinct contributions to quality information. To address this issue, we propose DisPA, a novel disentangled representation learning framework for NR-PCQA. The framework trains a dual-branch disentanglement network to minimize mutual information (MI) between representations of point cloud content and distortion. Specifically, to fully disentangle representations, the two branches adopt different philosophies: the content-aware encoder is pretrained by a masked auto-encoding strategy, which can allow the encoder to capture semantic information from rendered images of distorted point clouds; the distortion-aware encoder takes a mini-patch map as input, which forces the encoder to focus on low-level distortion patterns. Furthermore, we utilize an MI estimator to estimate the tight upper bound of the actual MI and further minimize it to achieve explicit representation disentanglement. Extensive experimental results demonstrate that DisPA outperforms state-of-the-art methods on multiple PCQA datasets.

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TMLR Journal 2023 Journal Article

Scalable Deep Compressive Sensing

Zhonghao Zhang
Yipeng Liu
Xingyu Cao
Fei Wen
Ce Zhu

Deep learning has been used to image compressive sensing (CS) for enhanced reconstruction performance. However, most existing deep learning methods train different models for different subsampling ratios, which brings an additional hardware burden. In this paper, we develop a general framework named scalable deep compressive sensing (SDCS) for the scalable sampling and reconstruction (SSR) of all existing end-to-end-trained models. In the proposed way, images are measured and initialized linearly. Two sampling matrix masks are introduced to flexibly control the subsampling ratios used in sampling and reconstruction, respectively. To achieve a reconstruction model with flexible subsampling ratios, a training strategy dubbed scalable training is developed. In scalable training, the model is trained with the sampling matrix and the initialization matrix at various subsampling ratios by integrating different sampling matrix masks. Experimental results show that models with SDCS can achieve SSR without changing their structure while maintaining good performance, and SDCS outperforms other SSR methods.

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