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Related Experiment Video

Updated: Apr 11, 2026

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

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Learn From Examples: In-Context Learning for Camouflaged Object Detection.

Chunyuan Chen, Weiyun Liang, Ji Du

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 9, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces in-context learning camouflaged object detection (ICL-COD), a new method using visual examples to improve camouflage perception. The ICL-Camo network effectively leverages contextual information for better camouflaged object recognition.

    Related Experiment Videos

    Last Updated: Apr 11, 2026

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

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    Published on: December 15, 2023

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Camouflaged object detection (COD) has advanced with paradigms like referring COD (Ref-COD) and collaborative COD (Co-COD).
    • Existing methods show limitations in effectively utilizing reference information for enhanced performance.
    • There is a need for novel approaches to improve the perception and recognition of camouflaged objects.

    Purpose of the Study:

    • To introduce a novel paradigm for camouflaged object detection called in-context learning camouflaged object detection (ICL-COD).
    • To propose the ICL-Camo network designed to leverage visual examples and annotations for improved camouflage perception.
    • To enhance the model's ability to recognize camouflaged objects by effectively utilizing contextual information.

    Main Methods:

    • Developed the in-context learning camouflaged object detection (ICL-COD) paradigm, utilizing visual examples and annotations.
    • Proposed the ICL-Camo network featuring a context mining module (CMM) to extract fine-grained contextual details from examples.
    • Integrated a context guiding module (CGM) to use mined contextual information for guiding attention towards potential camouflaged regions in target images.

    Main Results:

    • The proposed ICL-COD paradigm and ICL-Camo network demonstrated significant effectiveness on COD benchmarks.
    • Experiments confirmed the model's enhanced ability to perceive and recognize camouflaged objects.
    • The approach successfully leveraged contextual information from visual examples to improve detection performance.

    Conclusions:

    • The ICL-COD paradigm represents a novel and effective approach to camouflaged object detection.
    • The ICL-Camo network, with its CMM and CGM, successfully enhances camouflage perception by utilizing contextual guidance.
    • The findings suggest a promising direction for future research in leveraging in-context learning for challenging visual recognition tasks.