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Related Experiment Video

Updated: Nov 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

799

Investigating the Bilateral Connections in Generative Zero-Shot Learning.

Jingjing Li, Mengmeng Jing, Ke Lu

    IEEE Transactions on Cybernetics
    |February 3, 2021
    PubMed
    Summary

    This study introduces Boomerang-GAN for zero-shot learning (ZSL), enhancing computer vision by modeling bilateral connections between visual and semantic spaces. The novel approach improves recognition and segmentation for unseen categories.

    Related Experiment Videos

    Last Updated: Nov 19, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    799

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Zero-shot learning (ZSL) addresses recognizing unseen categories by leveraging semantic information.
    • Existing ZSL methods often focus on unidirectional mappings between visual and semantic spaces.
    • Real-world visual and semantic information exhibit a bidirectional relationship.

    Purpose of the Study:

    • To investigate and model the bilateral connections between visual and semantic spaces in ZSL.
    • To introduce a novel generative adversarial network (GAN) based model, Boomerang-GAN, for ZSL.
    • To improve the performance of ZSL models by considering a two-way relationship between visual features and semantic embeddings.

    Main Methods:

    • Developed Boomerang-GAN, a conditional generative adversarial network (GAN).
    • Generates unseen visual samples from semantic embeddings.
    • Incorporates a multimodal cycle-consistent loss to ensure visual features translate back to semantic embeddings.

    Main Results:

    • Boomerang-GAN demonstrates superior performance in both ZSL and generalized ZSL (GZSL) settings.
    • The model achieves state-of-the-art results on five benchmark datasets.
    • Effectiveness validated across both recognition and segmentation tasks.

    Conclusions:

    • Modeling bilateral visual-semantic relationships is crucial for advancing ZSL.
    • Boomerang-GAN offers a robust framework for generative ZSL.
    • The proposed method significantly enhances the ability to handle novel visual categories.