  Bandwidth constraints during signal acquisition frequently impede real-time
detection applications. Hyperspectral data is a notable example, whose vast
volume compromises real-time hyperspectral detection. To tackle this hurdle, we
introduce a novel approach leveraging pre-acquisition modulation to reduce the
acquisition volume. This modulation process is governed by a deep learning
model, utilizing prior information. Central to our approach is LUM-ViT, a
Vision Transformer variant. Uniquely, LUM-ViT incorporates a learnable
under-sampling mask tailored for pre-acquisition modulation. To further
optimize for optical calculations, we propose a kernel-level weight
binarization technique and a three-stage fine-tuning strategy. Our evaluations
reveal that, by sampling a mere 10% of the original image pixels, LUM-ViT
maintains the accuracy loss within 1.8% on the ImageNet classification task.
The method sustains near-original accuracy when implemented on real-world
optical hardware, demonstrating its practicality. Code will be available at
https://github.com/MaxLLF/LUM-ViT.
