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Updated: Jul 17, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Graph embedding based multi-label Zero-shot Learning.

Haigang Zhang1, Xianglong Meng1, Weipeng Cao2

  • 1Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic, Shenzhen, 518055, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 1, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach for multi-label zero-shot learning (ZSL) by leveraging label correlations through a directed semantic graph. This method enhances the recognition of unseen objects in complex images, improving ZSL model performance.

Keywords:
Feature embeddingKnowledge graphMulti-label classificationZero-shot Learning

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Standard single-label zero-shot learning (ZSL) is less realistic than multi-label ZSL, as natural images often contain multiple objects.
  • Intra-class feature entanglement hinders the alignment of visual and semantic features, limiting the recognition of unseen samples in multi-label ZSL.
  • Existing multi-label ZSL methods often overlook the crucial relationships between label semantics, focusing instead on visual feature refinement.

Purpose of the Study:

  • To address the limitations of current multi-label ZSL methods by incorporating label co-occurrence relationships.
  • To develop a novel approach that utilizes semantic information to improve the comprehensive recognition of unseen visual concepts.

Main Methods:

  • Constructed a directed weighted semantic graph utilizing category label co-occurrence statistics and prior knowledge.
  • Represented category semantics as node features and conditional probabilities of label co-occurrence as weighted edges within the graph.
  • Simultaneously updated and refined node features and edge weights to guide targeted visual feature extraction, integrating global and local perspectives into the feature network.

Main Results:

  • The proposed method demonstrated significant effectiveness on two challenging multi-label ZSL benchmarks: NUS-WIDE and Open Images.
  • Achieved an absolute performance gain of 2.4% mAP on the NUS-WIDE dataset compared to state-of-the-art models.
  • Obtained an absolute performance gain of 2.1% mAP on the Open Images dataset, outperforming existing methods.

Conclusions:

  • Leveraging label correlations through a semantic graph is a promising direction for advancing multi-label zero-shot learning.
  • The proposed method effectively addresses intra-class feature entanglement by incorporating semantic relationships, leading to improved recognition of unseen samples.
  • The approach offers a more comprehensive and complete understanding of complex visual scenes in multi-label ZSL scenarios.