graph construction
49 papers with code • 0 benchmarks • 3 datasets
Benchmarks
RoadTracer
CovidQA
IPRE
Greatest papers with code
DyNet: The Dynamic Neural Network Toolkit
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection
open-mmlab/mmocr
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CVPR 2020
In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection.
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
benedekrozemberczki/pytorch_geometric_temporal
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CVPR 2019
In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.
Ranked #3 on
Skeleton Based Action Recognition
on UAV-Human
Visualizing Large-scale and High-dimensional Data
We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space.
EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph
In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph.
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
lshiwjx/2s-AGCN
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Second, the second-order information of the skeleton data, i. e., the length and orientation of the bones, is rarely investigated, which is naturally more informative and discriminative for the human action recognition.
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
atcbosselut/comet-commonsense
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ACL 2019
We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017).
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
Learning to Segment 3D Point Clouds in 2D Image Space
Zhang-VISLab/Learning-to-Segment-3D-Point-Clouds-in-2D-Image-Space
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CVPR 2020
In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively and efficiently project such point clouds into a 2D image space so that traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation.
Ranked #5 on
3D Part Segmentation
on ShapeNet-Part
