Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams

Yun, Xiao-Long; Zhang, Yanming; Yin, Fei; Liu, Cheng‐Lin

doi:10.1109/tmm.2021.3087000

Cited by 28 publications

(15 citation statements)

References 54 publications

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“…Recently, modified GNN architectures have been developed for 3D instance segmentation on point clouds. There have been two main approaches: predicting bounding shapes to separate individual object instances after re-embedding the graph through edge and/or node convolutions [142,143] and (possibly attention based) graph pooling/clustering/condensation networks followed by localized node classification [144][145][146].…”

Section: Instance Segmentation Approachesmentioning

confidence: 99%

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Thais¹,

Calafiura²,

Chachamis³

et al. 2022

Preprint

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Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs. This allows a wide variety of high-and low-level physical features to be attached to measurements and, by the same token, a wide variety of HEP tasks to be accomplished by the same GNN architectures. GNNs have found powerful use-cases in reconstruction, tagging, generation and end-to-end analysis. With the wide-spread adoption of GNNs in industry, the HEP community is well-placed to benefit from rapid improvements in GNN latency and memory usage. However, industry use-cases are not perfectly aligned with HEP and much work needs to be done to best match unique GNN capabilities to unique HEP obstacles. We present here a range of these capabilities,

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Section: Instance Segmentation Approachesmentioning

confidence: 99%

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Thais¹,

Calafiura²,

Chachamis³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, due to the semisupervised nature of GAGAN itself, it does not provide any control over the generated symbolic information. Concerning the generation of continuous symbols, literature [18] have proposed a direct linear interpolation of two different symbol shapes using symbol feature points, and then these shapes are compressed into a one-dimensional coding vector using a fully connected network, and finally, the coding vector is fed into the adversarial network to generate the continuous symbols. Literature [19] also proposed the G2-GAN model for cultural symbol synthesis with symbolic feature points as the controllable condition.…”

Section: Symbol Generation Modelmentioning

confidence: 99%

“…In order to verify the validity of the model in this paper, comparison experiments are conducted with the current mainstream models A [17], B [25], C [18], and D [26], respectively, and analyzed under the same data and environment. e detailed data are shown in Table 2.…”

Section: Comparison Of Related Workmentioning

confidence: 99%

Research on the Creation of Chinese National Cultural Identity Symbols Based on Visual Images

Yang

2022

Mathematical Problems in Engineering

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Cultural symbol generation has always been a challenging task to achieve symbols that can represent national culture and promote people’s identification with Chinese culture. In this paper, we combine generative adversarial network (GAN) to propose a symbolic generation model of Chinese national cultural identity based on visual images. First, combining pattern search regular terms with generator cross-loss functions based on GAN generative adversarial networks to improve the pattern collapse phenomenon of generative adversarial networks. Second, the normal convolutional layer of the generator in the network structure is replaced with a deep-space separable convolution to improve the real-time performance of the model by reducing the model parameters. Through extensive testing on real datasets, the results show that the model in this paper can generate higher performance ethnic culture symbols while maintaining better temporal performance, which has some practical application value.

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“…And (Avelar et al, 2020) utilized the Graph Attention Networks to perform superpixel image classification. (Yun et al, 2021) proposed a GNN-based method for online handwritten diagram recognition. For the specific task of brain connectivity research, we know that regular images are the default data in most machine learning models.…”

Section: Gnn In Medical Imagesmentioning

confidence: 99%

A Survey of GNN in Bioinformation Data

Zhu¹

2022

Preprint

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With the development of data science, more and more machine learning technologies have been designed to solve complicated and challenging real-world tasks containing a large volume of data. And many significant real-world datasets contain data in the form of networks or graphs. Graph Neural Networks is one of the powerful machine learning tools, which could provide a perfect solution to processing a large amount of non-Euclidean data. And because most bio information data in bioinformatics is in the non-Euclidean domain, Graph Neural Networks could then directly be applied to solve problems in bioinformatics. Much research has been done in the field of GNN, and there are also some surveys related to GNN and its applications. However, there has been little research focusing on GNN in bioinformatics, and we think in the future we could better utilize GNN in the field of biology, so we would like to write a literature review to help take a comprehensive look at GNN and their applications in the field of bioinformatics. In this paper, we would first introduce SOTA models in Graph Neural Networks, and second, introduce their applications in bio information. And then we would provide future directions of Graph Neural Networks in bioinformatics.

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Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams

Cited by 28 publications

References 54 publications

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Research on the Creation of Chinese National Cultural Identity Symbols Based on Visual Images

A Survey of GNN in Bioinformation Data

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