Millions of people around the world experience blindness or moderate to severe visual disability, who need to rely on screen readers to perceive the content of phone screens. Guidelines and testing tools developed to aid software developers suffer from inconsistency in categorizing accessibility issues and not faithfully representing real user experience. In this paper, we aim to construct a better classification of accessibility issues, integrating feedback from screen reader users to existing computational methods. First, we conduct a systematic literature review, investigating 31 papers that demonstrated automated interventions for mobile accessibility. We juxtapose their computationally addressed issues with real user experience, by observing blind users’ interaction on 4 apps across 20 user studies. Synthesizing the two studies, we construct a categorization and guideline for screen reader accessibility issues on mobile, aimed to initiate a more user-aware understanding and subsequent interventions towards accessible mobile app development.

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 fine-tune networks for each task, Dr. RAW achieves state-of-the-art performance with minimal parameter updates. At the input stage, we apply lightweight pre-processing modules, sensor and illumination mapping, followed by re-mosaicing, to mitigate data inconsistencies stemming from sensor variation and lighting. At the network level, we introduce task-specific adaptation through two modules: Sensor Prior Prompts (SPP) and Low-Rank Adaptation (LoRA). SPP injects sensor-aware conditioning into the network via learnable prompts derived from imaging priors, while LoRA enables efficient task-specific tuning by updating only low-rank matrices in key backbone layers. Despite minimal tuning, our method delivers superior results across four RAW-based tasks (object detection, semantic segmentation, instance segmentation, and pose estimation) on nine datasets encompassing low-light and over-exposed conditions. By harnessing the intrinsic physical cues of RAW data alongside parameter-efficient techniques, our method advances RAW-based vision systems, achieving both high accuracy and computational economy. We will release our source code.