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Related Experiment Video

Updated: May 15, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

RAW-Adapter: Adapting Pre-Trained Visual Model to Camera RAW Images and a Benchmark.

Ziteng Cui, Jianfei Yang, Tatsuya Harada

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 13, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    RAW-Adapter enhances computer vision by using camera RAW images, preserving physical details lost in sRGB. This framework integrates learnable image signal processing (ISP) adapters for improved performance across diverse scenarios.

    Area of Science:

    • Computer Vision
    • Image Signal Processing
    • Machine Learning

    Background:

    • sRGB images are preferred for pre-training visual models due to ease of acquisition and storage.
    • Camera RAW images contain richer physical details crucial for real-world scenarios.
    • Existing methods often integrate image signal processing (ISP) with network modules, missing model-level synergies.

    Purpose of the Study:

    • To propose RAW-Adapter, a novel framework leveraging adapter-based methodologies for processing RAW image data.
    • To introduce RAW-Bench, a benchmark for evaluating RAW-based computer vision algorithms.
    • To enhance the performance and generalization ability of RAW-based models.

    Main Methods:

    • Developed RAW-Adapter, incorporating learnable ISP modules as input-level adapters.

    Related Experiment Videos

    Last Updated: May 15, 2026

    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

  • Integrated model-level adapters to bridge ISP processing with downstream architectures.
  • Introduced RAW-Bench with 17 types of RAW-based common corruptions.
  • Proposed a RAW-based data augmentation strategy.

    • Main Results:

    • RAW-Adapter demonstrated effectiveness and efficiency compared to state-of-the-art ISP methods.
    • Systematic comparison on RAW-Bench highlighted RAW-Adapter's robust performance.
    • Data augmentation improved RAW-Adapter's performance and out-of-domain generalization.

    Conclusions:

    • RAW-Adapter offers a general framework applicable to various computer vision systems.
    • The proposed method effectively utilizes the rich information in RAW images.
    • RAW-Adapter shows significant promise for advancing computer vision tasks using RAW data.