Edge-based Katz centralities for spatio-temporal multiplex networks
本文提出了一种基于边的Katz中心性计算方法，用于时空多层网络，并通过块三角和带状超邻接矩阵表示法来解决网络结构小变化导致中心性大幅变化的问题。
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Beyond Convolution: A Taxonomy of Structured Operators for Learning-Based Image Processing
Standard convolutions, due to their fixed structure, linearity, and reliance on local averaging, struggle to capture complex image characteristics such as low-rank structures, adaptive basis representations, and non-uniform spatial dependencies. This work proposes a unified taxonomy encompassing five classes of structured operators—decomposition-based, adaptive weighting, basis-adaptive, integral/kernel-based, and attention-based—and systematically analyzes their differences along key dimensions including locality, linearity, and equivariance. By leveraging techniques such as singular value/tensor decomposition, content-adaptive weighting, learnable analysis bases, position-dependent nonlinear kernels, and attention mechanisms, the study comprehensively evaluates the performance of these operators across image-to-image and image-to-label tasks. The findings clarify the respective strengths and limitations of each operator class, offering both theoretical insights and practical guidance for future research.
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Learning to Predict Aboveground Biomass from RGB Images with 3D Synthetic Scenes
Traditional methods struggle to accurately and cost-effectively estimate aboveground biomass (AGB) in dense forests. To address this, we propose the first end-to-end deep learning framework for dense AGB prediction from a single ground-level RGB image. Our key innovation is formulating pixel-wise AGB density maps as the regression target. We synthesize a large-scale 3D forest dataset—SPREAD—by leveraging instance segmentation masks and tree attributes (e.g., species, diameter at breast height) to generate pixel-level AGB ground truth via allometric equations. Critically, our method requires neither LiDAR nor multi-view imagery, greatly enhancing scalability and enabling broad public participation. Experiments demonstrate strong generalization: median absolute error is 1.22 kg/m² on the held-out SPREAD test set and 1.94 kg/m² on real-world forest images, confirming both accuracy and practical applicability.
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Automated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation
Automated detection and segmentation of cracks in cultural heritage artifacts (e.g., statues, monuments) remain challenging due to poor generalizability across diverse, unseen artifact types and conditions. To address this, we propose a semantic segmentation framework based on the U-Net architecture, systematically evaluating multiple CNN encoders—including ResNet and EfficientNet—for fine-grained, pixel-level crack delineation. Evaluated on the OmniCrack30k dataset, our models achieve state-of-the-art performance in mIoU, Dice, and Jaccard scores. Crucially, we demonstrate that pre-trained encoders exhibit strong cross-domain generalization to previously unseen heritage objects without artifact-specific fine-tuning. Furthermore, out-of-distribution qualitative validation on unlabeled real-world damaged images confirms robustness under varying lighting conditions, material textures, and scale variations. This work establishes a plug-and-play, high-accuracy, and highly adaptable paradigm for automated crack diagnosis in cultural heritage conservation.
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Optimal Weighted Convolution for Classification and Denosing
Standard convolutional neural networks neglect the relative spatial positions between neighboring and central pixels, limiting their capacity for spatial modeling. To address this, we propose a novel weighted convolution operator that embeds a learnable spatial density function directly into the convolution kernel—enabling adaptive, position-aware weight assignment based on relative coordinates—without increasing model parameters or compromising architectural compatibility. The operator is dimension-agnostic, supporting arbitrary-dimensional regular grid signals, and is designed for plug-and-play integration. Implemented in PyTorch with precomputation for efficient inference, it achieves significant improvements: on CIFAR-100, VGG accuracy increases by 10.05 percentage points to 66.94%; on the DIV2K image denoising benchmark, PSNR improves by 2.46 dB to 22.63—substantially outperforming standard convolution baselines.
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Edge-based Katz centralities for spatio-temporal multiplex networks
本文提出了一种基于边的Katz中心性计算方法，用于时空多层网络，并通过块三角和带状超邻接矩阵表示法来解决网络结构小变化导致中心性大幅变化的问题。
0 citationsRead paper
Beyond Convolution: A Taxonomy of Structured Operators for Learning-Based Image Processing
Standard convolutions, due to their fixed structure, linearity, and reliance on local averaging, struggle to capture complex image characteristics such as low-rank structures, adaptive basis representations, and non-uniform spatial dependencies. This work proposes a unified taxonomy encompassing five classes of structured operators—decomposition-based, adaptive weighting, basis-adaptive, integral/kernel-based, and attention-based—and systematically analyzes their differences along key dimensions including locality, linearity, and equivariance. By leveraging techniques such as singular value/tensor decomposition, content-adaptive weighting, learnable analysis bases, position-dependent nonlinear kernels, and attention mechanisms, the study comprehensively evaluates the performance of these operators across image-to-image and image-to-label tasks. The findings clarify the respective strengths and limitations of each operator class, offering both theoretical insights and practical guidance for future research.
0 citationsRead paper
Learning to Predict Aboveground Biomass from RGB Images with 3D Synthetic Scenes
Traditional methods struggle to accurately and cost-effectively estimate aboveground biomass (AGB) in dense forests. To address this, we propose the first end-to-end deep learning framework for dense AGB prediction from a single ground-level RGB image. Our key innovation is formulating pixel-wise AGB density maps as the regression target. We synthesize a large-scale 3D forest dataset—SPREAD—by leveraging instance segmentation masks and tree attributes (e.g., species, diameter at breast height) to generate pixel-level AGB ground truth via allometric equations. Critically, our method requires neither LiDAR nor multi-view imagery, greatly enhancing scalability and enabling broad public participation. Experiments demonstrate strong generalization: median absolute error is 1.22 kg/m² on the held-out SPREAD test set and 1.94 kg/m² on real-world forest images, confirming both accuracy and practical applicability.
0 citationsRead paper
Automated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation
Automated detection and segmentation of cracks in cultural heritage artifacts (e.g., statues, monuments) remain challenging due to poor generalizability across diverse, unseen artifact types and conditions. To address this, we propose a semantic segmentation framework based on the U-Net architecture, systematically evaluating multiple CNN encoders—including ResNet and EfficientNet—for fine-grained, pixel-level crack delineation. Evaluated on the OmniCrack30k dataset, our models achieve state-of-the-art performance in mIoU, Dice, and Jaccard scores. Crucially, we demonstrate that pre-trained encoders exhibit strong cross-domain generalization to previously unseen heritage objects without artifact-specific fine-tuning. Furthermore, out-of-distribution qualitative validation on unlabeled real-world damaged images confirms robustness under varying lighting conditions, material textures, and scale variations. This work establishes a plug-and-play, high-accuracy, and highly adaptable paradigm for automated crack diagnosis in cultural heritage conservation.
0 citationsRead paper
Optimal Weighted Convolution for Classification and Denosing
Standard convolutional neural networks neglect the relative spatial positions between neighboring and central pixels, limiting their capacity for spatial modeling. To address this, we propose a novel weighted convolution operator that embeds a learnable spatial density function directly into the convolution kernel—enabling adaptive, position-aware weight assignment based on relative coordinates—without increasing model parameters or compromising architectural compatibility. The operator is dimension-agnostic, supporting arbitrary-dimensional regular grid signals, and is designed for plug-and-play integration. Implemented in PyTorch with precomputation for efficient inference, it achieves significant improvements: on CIFAR-100, VGG accuracy increases by 10.05 percentage points to 66.94%; on the DIV2K image denoising benchmark, PSNR improves by 2.46 dB to 22.63—substantially outperforming standard convolution baselines.
0 citationsRead paper