Dr. RAW: Towards General High-Level Vision from RAW with Efficient Task Conditioning

We introduce Dr. RAW, a unified and tuning-efficient framework for high-level computer vision tasks directly operating on camera RAW data. Unlike previous approaches that optimize image signal processing (ISP) pipelines and fully finetune networks for each task, Dr. RAW achieves state-of-the-art performance with minimal parameter updates and frozen backbone weights. At the input stage, we apply lightweight pre-processing steps, including sensor and illumination mapping, along with re-mosaicing, to mitigate data inconsistencies stemming from sensor variations and lighting conditions. At the network level, we introduce task-specific adaptation through two modules: Sensor Prior Prompts (SPP) and task-specific Low-Rank Adaptation (LoRA). SPP injects sensor-aware conditioning into the network via learnable prompts derived from RAW pixel distribution priors, while LoRA enables efficient task-specific tuning by updating only low-rank matrices in key backbone layers. Despite minimal tuning, Dr. RAW delivers superior results across four RAW-based tasks (object detection, semantic segmentation, instance segmentation, and pose estimation) on nine datasets encompassing various light conditions.