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Claim this profileWorks380 from public data
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- Relation-Shape Convolutional Neural Network for Point Cloud Analysis1,050
RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration for point cloud analysis, achieves the state of the arts and a hierarchical architecture can be developed to achieve contextual shape-aware learning for Point cloud analysis.
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- AugFPN: Improving Multi-Scale Feature Learning for Object Detection588
This paper begins by first analyzing the design defects of feature pyramid in FPN, and then introduces a new feature pyramid architecture named AugFPN to address these problems, which consists of three components: Consistent Supervision, Residual Feature Augmentation, and Soft RoI Selection.
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- Spatio-Temporal Graph Structure Learning for Traffic Forecasting314
A novel framework named Structure Learning Convolution (SLC) is proposed that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting and significantly outperforms the state-of-the-art ones.
- DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing300
DensePoint is proposed, a general architecture to learn densely contextual representation for point cloud processing that extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns.
- Semantic labeling in very high resolution images via a self-cascaded convolutional neural network289
A novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet), for confusing manmade objects and fine-structured objects, ScasNet improves the labeling coherence with sequential global- to-local contexts aggregation.
- Spectral Unmixing via Data-Guided Sparsity276
This paper proposes a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel and applies the ℓp (0 <; p <; 1) constraint in an adaptive manner.
- Data guided Sparse Method for Hyperspectral Unmixing.257
This paper proposes a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel and applies the ℓp (0 <; p <; 1) constraint in an adaptive manner.
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- Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images201
A gated convolutional neural network is proposed that achieves competitive segmentation accuracy on the public ISPRS 2D Semantic Labeling benchmark, which is challenging for segmentation by only using the RGB images.
- GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction189
A Global Spatial-Temporal Network (GSTNet) is proposed, which consists of several layers of spatial-temporal blocks, which can simultaneously extract the dynamic temporal dependencies and the global spatial correlations.
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- Robust level set image segmentation via a local correntropy-based K-means clustering157
A novel segmentation algorithm via a local correntropy-based K-means (LCK) clustering algorithm that can be robust to the outliers is proposed, and is incorporated into the region-based level set segmentation framework.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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