# Adaptive Camera Sensor for Vision Models Source: [Adaptive Camera Sensor for Vision Models]() Collection index: [llms.txt]() Data updated: 2026-05-21T05:00:05.413Z Authors: Eunsu Baek, Sunghwan Han, Taesik Gong, Hyung-Sin Kim Published: 2025-03-04T01:20:23.000Z ## Abstract Domain shift remains a persistent challenge in deep-learning-based computer vision, often requiring extensive model modifications or large labeled datasets to address. Inspired by human visual perception, which adjusts input quality through corrective lenses rather than over-training the brain, we propose Lens, a novel camera sensor control method that enhances model performance by capturing high-quality images from the model's perspective rather than relying on traditional human-centric sensor control. Lens is lightweight and adapts sensor parameters to specific models and scenes in real-time. At its core, Lens utilizes VisiT, a training-free, model-specific quality indicator that evaluates individual unlabeled samples at test time using confidence scores without additional adaptation costs. To validate Lens, we introduce ImageNet-ES Diverse, a new benchmark dataset capturing natural perturbations from varying sensor and lighting conditions. Extensive experiments on both ImageNet-ES and our new ImageNet-ES Diverse show that Lens significantly improves model accuracy across various baseline schemes for sensor control and model modification while maintaining low latency in image captures. Lens effectively compensates for large model size differences and integrates synergistically with model improvement techniques. Our code and dataset are available at github.com/Edw2n/Lens.git. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet IN-1K IN-21K (val)](): Image Classification; ImageNet IN-1K IN-21K (val); Accuracy: not recorded. - [Image Classification on ImageNet-ES (test)](): Image Classification; ImageNet-ES (test); Oracle (S): not recorded. - [Image Classification on ImageNet-ES Diverse new (test)](): Image Classification; ImageNet-ES Diverse new (test); Oracle Score (S): not recorded. ## Original sources and implementation [Original paper]() [PDF]()