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Aggregates Classification01:29

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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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Related Experiment Video

Updated: Nov 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

845

Vocabulary-Wide Credit Assignment for Training Image Captioning Models.

Han Liu, Shifeng Zhang, Ke Lin

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 20, 2021
    PubMed
    Summary

    This study introduces vocabulary-wide credit assignment for reinforcement learning in image captioning. This new method improves model training by assigning credit to each vocabulary word at every generation step.

    Related Experiment Videos

    Last Updated: Nov 20, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    845

    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Reinforcement learning (RL) is effective for training image captioning models.
    • Existing RL methods for image captioning use sentence-level or word-level credit assignment.
    • A need exists for more granular credit assignment in RL for image captioning.

    Purpose of the Study:

    • To propose a novel credit assignment method for RL-based image captioning.
    • To introduce Vocabulary-Critical Sequence Training (VCST) based on the new method.
    • To enhance the performance of existing RL training frameworks for image captioning.

    Main Methods:

    • Developed a 'vocabulary-wide credit assignment' method, assigning credit to each vocabulary word per generation step.
    • Proposed Vocabulary-Critical Sequence Training (VCST) integrating this new credit assignment approach.
    • Integrated VCST into existing RL training methods for image captioning models.

    Main Results:

    • Extensive experiments demonstrated the effectiveness of VCST.
    • VCST achieved improved results when incorporated into popular image captioning models.
    • The proposed vocabulary-wide credit assignment method offers a new perspective on credit allocation in RL.

    Conclusions:

    • Vocabulary-wide credit assignment and VCST represent a significant advancement in RL for image captioning.
    • The proposed method is orthogonal to existing sentence-level and word-level approaches.
    • VCST offers a versatile and effective enhancement for training image captioning models.