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Claim this profileWorks144 from public data
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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.
- 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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- 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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- Enhanced Boundary Learning for Glass-like Object Segmentation123
This paper proposes a novel refined differential module that outputs finer boundary cues and introduces an edge-aware point-based graph convolution network module to model the global shape along the boundary.
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- Pro-Tuning: Unified Prompt Tuning for Vision Tasks120
Comprehensive experiments evidence that the proposed Pro-tuning outperforms fine-tuning on a broad range of vision tasks and scenarios, including image classification, class imbalance, image corruption, natural adversarial examples, and out-of-distribution generalization.
- Scene text detection and recognition with advances in deep learning: a survey114
A thorough and in-depth reviews on the recent advances on scene text detection and recognition, mainly focusing on the methods that appeared in the past 5 years for text detection and recognition in images and videos, including the recent state-of-the-art techniques on the following three related topics.
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- DetNAS: Neural Architecture Search on Object Detection.94
This paper proposes DetNAS to automatically search neural architectures for the backbones of object detectors, formulated into a supernet and the search method relies on evolution algorithm (EA).
- Stitcher: Feedback-driven Data Provider for Object Detection76
Stitcher is presented, a feedback-driven data provider, which aims to train object detectors in a balanced way, and steadily improves performance by a large margin in all settings, especially for small objects, with nearly no additional computation in both training and testing stages.
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- Learning graph structure via graph convolutional networks57
A method that learns Graph Structure via graph Convolutional Networks (GSCN), which introduces the graph structure parameters measuring the correlation degrees of adjacent nodes, which is better to handle the graph-structured data of non-stationarity.
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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