Edge Classification
13 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Edge Classification
Most implemented papers
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
IBM/EvolveGCN
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Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane.
Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
mobarakol/Surgical_SceneGraph_Generation
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Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery.
Edge-augmented Graph Transformers: Global Self-attention is Enough for Graphs
shamim-hussain/egt_pytorch
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The resultant framework, which we call Edge-augmented Graph Transformer (EGT), can directly accept, process and output structural information as well as node information.
Dynamic Graph Convolutional Networks Using the Tensor M-Product
IBM/TM-GCN
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ICLR 2020
In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.
Shearlets as Feature Extractor for Semantic Edge Detection: The Model-Based and Data-Driven Realm
arsenal9971/shearlet_semantic_edge
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This is based on the fact that edges in images contain most of the semantic information.
Deep Multi-Scale Feature Learning for Defocus Blur Estimation
alikaraali/depthedgeawarebenet
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This paper presents an edge-based defocus blur estimation method from a single defocused image.
Real-Time Edge Classification: Optimal Offloading under Token Bucket Constraints
To deploy machine learning-based algorithms for real-time applications with strict latency constraints, we consider an edge-computing setting where a subset of inputs are offloaded to the edge for processing by an accurate but resource-intensive model, and the rest are processed only by a less-accurate model on the device itself.
Unmixing Convolutional Features for Crisp Edge Detection
WHUHLX/CATS
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This paper presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge detectors is mainly caused by the mixing phenomenon of convolutional neural networks: feature mixing in edge classification and side mixing during fusing side predictions.
Charged particle tracking via edge-classifying interaction networks
GageDeZoort/interaction_network_paper
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Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics.
