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Related Experiment Video

Updated: Jun 3, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Physically Guided Diffusion Framework With Neural Information Bottleneck Regulation for Robust Small Object

Yongcheng Zhou, Shilei Tan, Wei Li

    IEEE Transactions on Neural Networks and Learning Systems
    |June 1, 2026
    PubMed
    Summary
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    A new framework improves small object detection in challenging conditions like rain and low light. The neural information bottleneck with physics-guided diffusion enhances accuracy and robustness in adverse environments.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Small object detection in adverse conditions like haze, rain, and low light is difficult due to physical degradations and unstable features.
    • Existing detectors show degraded accuracy and poor generalization in these challenging environments.

    Purpose of the Study:

    • To propose a unified neural information bottleneck with physics-guided diffusion (NIB-PGD) framework for robust small object detection.
    • To enhance feature encoding and noise-resilient object localization by coupling physical priors with information-theoretic regularization.

    Main Methods:

    • Utilized a multiscale enhance-fuse-context attention (EFC-A) encoder for hierarchical semantics and contextual dependencies.
    • Implemented physics-guided feature-level diffusion with realistic degradation kernels in the noise schedule.

    Related Experiment Videos

    Last Updated: Jun 3, 2026

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
    10:20

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

    Published on: September 5, 2019

  • Employed box-level diffusion for iterative denoising and bounding box refinement without anchors or queries.
  • Applied a second-order information bottleneck (2O-IB) constraint to regulate mutual information and preserve task-relevant semantics.

    • Main Results:

    • Achieved 52.3% AP and 35.7% AP$_{s}$ on a composite benchmark.
    • Outperformed the strongest AP baseline by +1.2 AP and the strongest AP$_{s}$ baseline by +1.1 AP$_{s}$ .

    Conclusions:

    • The NIB-PGD framework demonstrates state-of-the-art performance in detection accuracy, robustness, and generalization.
    • The proposed approach effectively addresses the challenges of small object detection in adverse environments.