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Related Experiment Video

Updated: Oct 4, 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

705

Cross-modal distribution alignment embedding network for generalized zero-shot learning.

Qin Li1, Mingzhen Hou2, Hong Lai1

  • 1School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semantic embedding network for generalized zero-shot learning (GZSL). The method improves classification accuracy by encoding discriminative information and aligning feature distributions, outperforming existing approaches.

Keywords:
Generalized zero-shot learningImage classificationWeakly-supervised learning

Related Experiment Videos

Last Updated: Oct 4, 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

705

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Generalized Zero-Shot Learning (GZSL) faces challenges due to inconsistent image and class embedding spaces.
  • Existing GZSL methods struggle with semantic overlap, leading to misclassification of unseen classes as seen classes.

Purpose of the Study:

  • To develop a novel semantic embedding network for more discriminative knowledge transfer in GZSL.
  • To enhance the robustness and accuracy of GZSL by addressing semantic overlap and distribution inconsistencies.

Main Methods:

  • A novel semantic embedding network encodes discriminative information from attributes to visual semantic embeddings.
  • A distribution alignment constraint ensures consistency between learned embeddings and real image features.
  • An auxiliary classifier and a relation network are employed for improved embedding quality and flexible classification.

Main Results:

  • The proposed method demonstrates superior performance compared to state-of-the-art approaches in GZSL tasks.
  • Experimental results validate the effectiveness of the semantic embedding network and distribution alignment.

Conclusions:

  • The developed approach effectively tackles the challenges of semantic overlap and distribution inconsistency in GZSL.
  • The novel network architecture and constraints lead to significant improvements in classifying unseen classes.