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Shuyuan Yang

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9 papers

1 author row

AAAI Conference 2026 Conference Paper

Evolving Semantic Propagation for Aerial Semantic 3D Gaussian Splatting

Zihan Gao
Lingling Li
Xu Liu
Fang Liu
Licheng Jiao
Puhua Chen
Wenping Ma
Shuyuan Yang

Semantic understanding of large-scale aerial scenes represents a critical challenge in 3D computer vision, hindered by the prohibitive cost of dense annotation. This paper introduces EvoPropGS, a novel approach for the semantic segmentation of 3D Gaussian Splatting models that requires only minimal supervision. Our core insight is to leverage the inherent structural repetitions within aerial environments to propagate semantic information from a sparse set of annotations across the entire 3D scene. Our approach constructs a prompt library by pairing SAM-generated mask candidates with DINOv2 feature embeddings from annotated views. For unannotated regions, we generate pseudo-labels by matching region proposals with these featured prompts via cosine similarity. We then formulate optimal prompt selection as a discrete optimization problem solved via evolutionary search, guided by our novel fitness function that evaluates both 3D consistency and 2D semantic coherence. Extensive experiments demonstrate that EvoPropGS achieves accurate segmentation with only 2 percent annotated pixels.

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AAAI Conference 2024 Conference Paper

Harnessing the Power of SVD: An SVA Module for Enhanced Signal Classification

Lei Zhai
Shuyuan Yang
Yitong Li
Zhixi Feng
Zhihao Chang
Quanwei Gao

Deep learning methods have achieved outstanding performance in various signal tasks. However, due to degraded signals in real electromagnetic environment, it is crucial to seek methods that can improve the representation of signal features. In this paper, a Singular Value decomposition-based Attention, SVA is proposed to explore structure of signal data for adaptively enhancing intrinsic feature. Using a deep neural network as a base model, SVA performs feature semantic subspace learning through a decomposition layer and combines it with an attention layer to achieve adaptive enhancement of signal features. Moreover, we consider the gradient explosion problem brought by SVA and optimize SVA to improve the stability of training. Extensive experimental results demon-strate that applying SVA to a generalized classification model can significantly improve its ability in representations, making its recognition performance competitive with, or even better than, the state-of-the-art task-specific models.

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NeurIPS Conference 2024 Conference Paper

Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis

Qiang Wu
Gechang Yao
Zhixi Feng
Shuyuan Yang

Time series analysis finds wide applications in fields such as weather forecasting, anomaly detection, and behavior recognition. Previous methods attempted to model temporal variations directly using 1D time series. However, this has been quite challenging due to the discrete nature of data points in time series and the complexity of periodic variation. In terms of periodicity, taking weather and traffic data as an example, there are multi-periodic variations such as yearly, monthly, weekly, and daily, etc. In order to break through the limitations of the previous methods, we decouple the implied complex periodic variations into inclusion and overlap relationships among different level periodic components based on the observation of the multi-periodicity therein and its inclusion relationships. This explicitly represents the naturally occurring pyramid-like properties in time series, where the top level is the original time series and lower levels consist of periodic components with gradually shorter periods, which we call the periodic pyramid. To further extract complex temporal variations, we introduce self-attention mechanism into the periodic pyramid, capturing complex periodic relationships by computing attention between periodic components based on their inclusion, overlap, and adjacency relationships. Our proposed Peri-midFormer demonstrates outstanding performance in five mainstream time series analysis tasks, including short- and long-term forecasting, imputation, classification, and anomaly detection.

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EAAI Journal 2021 Journal Article

Graph Convolutional Neural Networks with Geometric and Discrimination information

Ronghua Shang
Yang Meng
Weitong Zhang
Fanhua Shang
Licheng Jiao
Shuyuan Yang

In recent years, geometric deep learning methods have been proposed, which are called Graph Convolutional Neural Networks (GCNNs). GCNNs not only can extract effective features like the classical CNN, but also can effectively reflect the true geometric structure of original data. Although GCNNs consider the geometric structure of original data, they construct the same feature graph to perform graph convolution, and ignore the difference between the local structures of different samples. Therefore, a novel Graph Convolutional Neural Network with Geometric and Discrimination information (GDGCNN) is proposed, which integrates traditional machine learning ideas to further improve the performance of feature extraction. In order to exploit differences between the local structures of different samples and make full use of the geometric structure of original data, GDGCNN constructs different feature graphs for different training batches to fully exploit the local geometry of data. Moreover, the discriminant regularization is introduced into GDGCNN to effectively utilize the discriminant information contained in original data. Therefore, GDGCNN has good discriminative ability and robustness. The experimental results show that GDGCNN can perform feature extraction tasks very well, and it is superior to some existing methods for classification in terms of accuracy and F1-Score.

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EAAI Journal 2018 Journal Article

Dual-graph regularized non-negative matrix factorization with sparse and orthogonal constraints

Yang Meng
Ronghua Shang
Licheng Jiao
Wenya Zhang
Shuyuan Yang

Semi-supervised Non-negative Matrix Factorization (NMF) can not only utilize a fraction of label information, but also effectively learn local information of the objectives, such as documents and faces. Semi-supervised NMF is an efficient technique for dimensionality reduction of high dimensional data. In this paper, we propose a novel semi-supervised NMF, called Dual-graph regularized Non-negative Matrix Factorization with Sparse and Orthogonal constraints (SODNMF). Dual-graph model is added into semi-supervised NMF, and the manifold structures of the data space and the feature space are taken into account simultaneously. In addition, the sparse constraint is used in SODNMF, which can simplify the calculation and accelerate the processing speed. The most important is that SODNMF makes use of bi-orthogonal constraints, which can avoid the non-correspondence between images and basic vectors. Therefore, it can effectively enhance the discrimination and the exclusivity of clustering, and improve the clustering performance. We give the objective function, the iterative updating rules and the convergence proof. Empirical experiments demonstrate encouraging results of our novel algorithm in comparison to four algorithms within some state-of-the-art algorithms through a set of evaluations based on three real datasets.

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JBHI Journal 2015 Journal Article

Sparsity-Inspired Nonparametric Probability Characterization for Radio Propagation in Body Area Networks

Xiaodong Yang
Shuyuan Yang
Qammer Hussain Abbasi
Zhiya Zhang
Aifeng Ren
Wei Zhao
Akram Alomainy

Parametric probability models are common references for channel characterization. However, the limited number of samples and uncertainty of the propagation scenario affect the characterization accuracy of parametric models for body area networks. In this paper, we propose a sparse nonparametric probability model for body area wireless channel characterization. The path loss and root-mean-square delay, which are significant wireless channel parameters, can be learned from this nonparametric model. A comparison with available parametric models shows that the proposed model is very feasible for the body area propagation environment and can be seen as a significant supplement to parametric approaches.

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EAAI Journal 2014 Journal Article

Improved Bandelet with heuristic evolutionary optimization for image compression

Shuyuan Yang
Shigang Wang
Zhengkang Liu
Min Wang
Licheng Jiao

As an efficient geometric analysis tool, Bandelet has exhibited enormous potential in image compression for its capability in capturing the geometrical structure in images. However, in available implementation of Bandelet, the optimal geometric flows are determined by a coarse and exhaustive search, which will degrade the performance of Bandelet in an accurate representation of images. In this paper, we advance a new image compression approach based on Laplacian Pyramid (LP) and improved Bandelet, where a Heuristic Memetic Algorithm (HMA) is proposed to locate accurate geometric flows. The chromosome is defined to represent geometric flows, and local and heuristic evolution operators are employed to make fast search possible. The improved Bandelet is used to compress the high-frequency band of the image decomposed by LP, and the low-frequency band is coded by Set Partitioning In Hierarchical Trees (SPIHT) algorithm. Some experiments are taken on some natural images and remote sensing images, and the results demonstrate that our proposed scheme outperforms SPIHT and the second generation Bandelet(2 G-Bandelet) in both PSNR and time consumption at low bit rate compression.

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EAAI Journal 2013 Journal Article

Superpixel-wise semi-supervised structural sparse coding classifier for image segmentation

Shuyuan Yang
Yuan Lv
Yu Ren
Licheng Jiao

Sparse coding based classifier (SCC) proves to lead to the state-of-the-art result in pattern recognition. Compared with traditional generative models and discriminative models, it neither casts some assumption on the distribution of data, nor learns a hyperplane to separate samples. However, SCC is characteristic of slow prediction because an l 0 -norm minimization need to be solved to assign the label for each sample. In this paper, we propose a Superpixel-wise Structural Sparse Coding based Classifier (S3CC) for image segmentation. An unsupervised superpixel segmentation is first used to derive the initial labeled samples, and SCC is extended to the semi-supervised pattern where unlabeled samples are incrementally labeled and taken as the dictionary to improve the classification accuracy. Moreover, a neighborhood spatial constraint is cast on the prediction of pixel labels, to avoid the speckle-like mis-segmentation of images. Some experiments are taken on some artificial texture images, to investigate the segmentation result of our proposed S3CC. Some aspects including (1) Comparison of S3CC with SCC, (2) Comparisons of S3CC with and without spatial constraint, (3) Comparison of S3CC with semi-supervised S3CC, are tested, and the results prove the efficiency and superiority of S3CC to its counterparts.

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EAAI Journal 2012 Journal Article

Evolution-enhanced multiscale overcomplete dictionaries learning for image denoising

Shuyuan Yang
Min Wang
Meirong Wei
Licheng Jiao

In this paper, a multiscale overcomplete dictionary learning approach is proposed for image denoising by exploiting the multiscale property and sparse representation of images. The images are firstly sparsely represented by a translation invariant dictionary and then the coefficients are denoised using some learned multiscale dictionaries. Dictionaries learning can be reduced to a non-convex l 0-norm minimization problem with multiple variables, so an evolution-enhanced algorithm is proposed to alternately optimize the variables. Some experiments are taken on comparing the performance of our proposed method with its counterparts on some benchmark natural images, and the superiorities of our proposed method to its counterparts can be observed in both the visual result and some numerical guidelines.

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