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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Transformer-Based Capsule Network for 3D Part-Whole Relationship Learning.

Yu Chen1, Jieyu Zhao1, Qilu Qiu1

  • 1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China.

Entropy (Basel, Switzerland)
|May 28, 2022
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Summary
This summary is machine-generated.

This study introduces a novel 3D shape Transformer for object recognition, effectively learning part-to-whole relationships in 3D models. The model excels at complex 3D model recognition and feature learning from large datasets.

Keywords:
3D shape transformerdeformable 3D objectlocal-to-global cognitionshape-Transformer-based capsule

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Recognizing the relationship between object parts and the whole is a complex challenge in computer vision.
  • Recent advancements in Transformer models for Natural Language Processing (NLP) and image analysis offer new avenues for 3D model understanding.

Purpose of the Study:

  • To design a novel neural network for local-to-global cognition of 3D models.
  • To aggregate structural contextual features in 3D space for improved 3D model recognition.

Main Methods:

  • A 3D shape Transformer model was developed, utilizing local shape representations and proposing 'local shape tokens' to encode geometric information.
  • An iterative capsule routing algorithm, integrated with the Transformer, aggregates local shape information into high-level capsules for holistic understanding.

Main Results:

  • The model achieved profound results on deformable 3D object datasets (SHREC10, SHREC15) and the large ModelNet40 dataset.
  • Demonstrated excellent performance in complex 3D model recognition and feature learning from large-scale data.

Conclusions:

  • The proposed 3D shape Transformer effectively captures local-to-global relationships in 3D models.
  • The model shows significant potential for advanced 3D object recognition and analysis tasks.