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September 10, 2025IEEE Transactions on Neural Networks and Learning Systems

Toward Disentangled and Controllable Deep Metric Learning With Human-Like Concept Decomposition

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Authors

SCShuhuang ChenChina National Chemical Engineering (China)SCShiming ChenMohamed bin Zayed University of Artificial IntelligenceSYShuo YeBayan College

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Overview

Novel method achieves disentangled visual concepts in deep metric learning, suggesting improved interpretability.

Key Points

  • CMN effectively disentangles visual concepts, enhancing the interpretability of image embeddings.
  • State-of-the-art performance in image retrieval applications demonstrates significant advancements over existing methods.
  • The cross-attention mechanism associates concept vectors with regional visual features, improving control over embeddings.
  • Methodological innovations allow for more flexible and controllable applications in deep metric learning.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68c19f9c54b1d3bfb60db2dfhttps://doi.org/10.1109/tnnls.2025.3587907
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