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Related Experiment Video

Updated: Mar 29, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K

Local-Global Aware Concept Bottleneck Models for Interpretable Image Classification.

Ci Liu1, Zijie Lin1, Chen Tang1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces the Local-Global Aware Concept Bottleneck Model (LGA-CBM) to enhance interpretable image classification. LGA-CBM improves concept prediction accuracy and interpretability, crucial for remote sensing and medical imaging applications.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Concept Bottleneck Models (CBMs) offer interpretable image classification but struggle with CLIP's biases.
  • CLIP's global representation and lack of region sensitivity limit CBMs in critical sensor-driven fields.
  • Remote sensing and medical imaging require localized visual evidence for accurate classification.

Purpose of the Study:

  • To develop an improved Concept Bottleneck Model (CBM) addressing CLIP's limitations.
  • To enhance concept prediction accuracy and reliability for interpretable image classification.
  • To create a model suitable for sensor-driven applications demanding localized visual understanding.

Main Methods:

  • Proposed the Local-Global Aware Concept Bottleneck Model (LGA-CBM) with a training-free refinement pipeline.
Keywords:
concept bottleneck modelsfew-shot learningimage classificationinterpretable artificial intelligence

Related Experiment Videos

Last Updated: Mar 29, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K
  • Introduced Dual Masking Guided Concept Score Refinement (DMCSR) using attention weights for region-concept alignment.
  • Implemented Local-to-Global Concept Reidentification (L2GCR) and Similar Concepts Correction Mechanism (SCCM) with Grounding DINO for disambiguation.

    • Main Results:

    • LGA-CBM demonstrated state-of-the-art performance across six benchmark datasets.
    • Achieved superior accuracy and interpretability compared to existing methods.
    • Generated explanations that closely align with human cognitive understanding.

    Conclusions:

    • LGA-CBM effectively refines concept scores, overcoming CLIP's limitations.
    • The model provides highly interpretable image classification with minimal concept usage.
    • LGA-CBM shows significant promise for applications in remote sensing and medical imaging.