Adaptive Sensing for DNNs

2026

1. WACV

   ImageNet-sES: A First Systematic Study of Sensor-Environment Simulation Anchored by Real Recaptures

   Ji-yoon Kim, Eunsu Baek, and Hyung-Sin Kim

   In The IEEE/CVF Winter Conference on Applications of Computer Vision 2026 (WACV 2026) , Mar 2026

   Bib

   @inproceedings{ses,
     author = {Kim, Ji-yoon and Baek, Eunsu and Kim, Hyung-Sin},
     title = {ImageNet-sES: A First Systematic Study of Sensor-Environment Simulation Anchored by Real Recaptures},
     booktitle = {The IEEE/CVF Winter Conference on Applications of Computer Vision 2026 (WACV 2026)},
     year = {2026},
     month = mar,
     tags = {s4d}
   }

2025

1. NeuIPS

   

   AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift

   Eunsu Baek, Keondo Park, JeongGil Ko, and 3 more authors

   In The Thirty-Ninth Annual Conference on Neural Information Processing Systems Position Paper Track , Dec 2025

   Abs Bib PDF

   Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness. Despite notable successes, this paradigm incurs significant environmental, economic, and ethical costs, limiting sustainability and equitable access. Inspired by biological sensory systems, where adaptation occurs dynamically at the input (e.g., adjusting pupil size, refocusing vision)–we advocate for adaptive sensing as a necessary and foundational shift. Adaptive sensing proactively modulates sensor parameters (e.g., exposure, sensitivity, multimodal configurations) at the input level, significantly mitigating covariate shifts and improving efficiency. Empirical evidence from recent studies demonstrates that adaptive sensing enables small models (e.g., EfficientNet-B0) to surpass substantially larger models (e.g., OpenCLIP-H) trained with significantly more data and compute. We (i) outline a roadmap for broadly integrating adaptive sensing into real-world applications spanning humanoid, healthcare, autonomous systems, agriculture, and environmental monitoring, (ii) critically assess technical and ethical integration challenges, and (iii) propose targeted research directions, such as standardized benchmarks, real-time adaptive algorithms, multimodal integration, and privacy-preserving methods. Collectively, these efforts aim to transition the AI community toward sustainable, robust, and equitable artificial intelligence systems.

   @inproceedings{adaptivesensing,
     author = {Baek, Eunsu and Park, Keondo and Ko, JeongGil and Oh, Min-hwan and Gong, Taesik and Kim, Hyung-Sin},
     title = {AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift},
     booktitle = {The Thirty-Ninth Annual Conference on Neural Information Processing Systems Position Paper Track
     },
     year = {2025},
     month = dec,
     tags = {s4d}
   }

2. ICLR

   

   Adaptive Camera Sensor for Vision Model

   Eunsu Baek, Taesik Gong, and Hyung‑Sin Kim

   In The 13th International Conference on Learning Representations , Apr 2025

   Abs Bib PDF Code

   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.

   @inproceedings{lens,
     author = {Baek, Eunsu and Gong, Taesik and Kim, Hyung‑Sin},
     title = {Adaptive Camera Sensor for Vision Model},
     booktitle = {The 13th International Conference on Learning Representations},
     year = {2025},
     month = apr,
     tags = {s4d}
   }

2024

1. CVPR

   

   Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains

   Eunsu Baek, Keondo Park, Jiyoon Kim, and 1 more author

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2024

   Abs Bib PDF Video Code

   Computer vision applications predict on digital images acquired by a camera from physical scenes through light. However, conventional robustness benchmarks rely on perturbations in digitized images, diverging from distribution shifts occurring in the image acquisition process. To bridge this gap, we introduce a new distribution shift dataset, ImageNet-ES, comprising variations in environmental and camera sensor factors by directly capturing 202k images with a real camera in a controllable testbed. With the new dataset, we evaluate out-of-distribution (OOD) detection and model robustness. We find that existing OOD detection methods do not cope with the covariate shifts in ImageNet-ES, implying that the definition and detection of OOD should be revisited to embrace real-world distribution shifts. We also observe that the model becomes more robust in both ImageNet-C and -ES by learning environment and sensor variations in addition to existing digital augmentations. Lastly, our results suggest that effective shift mitigation via camera sensor control can significantly improve performance without increasing model size. With these findings, our benchmark may aid future research on robustness, OOD, and camera sensor control for computer vision. Our code and dataset are available at this https://github.com/Edw2n/ImageNet-ES

   @inproceedings{imagenet-es,
     author = {Baek, Eunsu and Park, Keondo and Kim, Jiyoon and Kim, Hyung-Sin},
     title = {Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
     year = {2024},
     month = jun,
     tags = {s4d}
   }