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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Video

Updated: Jun 21, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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TCFormer: Visual Recognition via Token Clustering Transformer.

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    |July 11, 2024
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    Summary
    This summary is machine-generated.

    This study introduces the Token Clustering Transformer (TCFormer), a novel approach that uses dynamic vision tokens to improve image analysis. TCFormer enhances performance by focusing on semantic meaning rather than fixed grids.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Transformers are state-of-the-art in computer vision, typically dividing images into fixed grids for vision tokens.
    • This fixed grid approach overlooks semantic details, leading to suboptimal performance in various vision tasks.

    Purpose of the Study:

    • To propose a new Transformer model, the Token Clustering Transformer (TCFormer), that generates dynamic vision tokens based on semantic meaning.
    • To improve the efficiency and effectiveness of vision Transformers by addressing the limitations of fixed tokenization.

    Main Methods:

    • Developed TCFormer, a model that creates dynamic vision tokens by clustering semantically similar image regions.
    • Implemented two key features: grouping non-adjacent similar regions and using finer tokens for detailed areas.

    Main Results:

    • TCFormer demonstrated significant effectiveness across diverse computer vision applications.
    • Evaluated performance in image classification, human pose estimation, semantic segmentation, and object detection.

    Conclusions:

    • The proposed TCFormer effectively addresses the limitations of fixed grid tokenization in vision Transformers.
    • Dynamic token generation based on semantic meaning offers a more robust and efficient approach for computer vision tasks.