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[ECCV 2024] RAW-Adapter: Adapting Pre-trained Visual Model to Camera RAW Images (Paper) (Website) (Zhihu中文解读)

Ziteng Cui¹, Tatsuya Harada^1,2.

^1.The University of Tokyo, ^2.RIKEN AIP

2024.11.04: Fix some config problems (path) in detection part.

2024.08.26 : Upload code of our paper.

2024.07.04 : Paper accepted by ECCV 2024 !

🚀: Abstract

sRGB images are now the predominant choice for pre-training visual models in computer vision research, owing to their ease of acquisition and efficient storage. Meanwhile, the advantage of RAW images lies in their rich physical information under variable real-world lighting conditions. For computer vision tasks directly based on camera RAW data, most existing studies adopt methods of integrating image signal processor (ISP) with backend networks, yet often overlook the interaction capabilities between the ISP stages and subsequent networks. Drawing inspiration from ongoing adapter research in NLP and CV areas, we introduce RAW-Adapter, a novel approach aimed at adapting sRGB pre-trained models to camera RAW data. RAW-Adapter comprises input-level adapters that employ learnable ISP stages to adjust RAW inputs, as well as model-level adapters to build connections between ISP stages and subsequent high-level networks. Additionally, RAW-Adapter is a general framework that could be used in various computer vision frameworks. Abundant experiments under different lighting conditions have shown our algorithm’s state-of-the-art (SOTA) performance, demonstrating its effectiveness and efficiency across a range of real-world and synthetic datasets.

Usage:

For object detection part:

cd mmdetection_github

For semantic segmentation part:

cd mmsegmentation_github

Citation:

If you use our dataset or find our work useful in your project, please consider to cite our paper, thx ~

@inproceedings{raw_adapter,
  title = {RAW-Adapter: Adapting Pretrained Visual Model to Camera RAW Images},
  author = {Ziteng Cui and Tatsuya Harada},
  booktitle={ECCV},
  year={2024}
}