HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis
<|reference_start|>HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis: Hyperspectral Imaging (HSI) plays an increasingly critical role in precise vision tasks within remote sensing, capturing a wide spectrum of visual data. Transformer architectures have significantly enhanced HSI task performance, while advancements in Transformer Architecture Search (TAS) have improved model discovery. To harness these advancements for HSI classification, we make the following contributions: i) We propose HyTAS, the first benchmark on transformer architecture search for Hyperspectral imaging, ii) We comprehensively evaluate 12 different methods to identify the optimal transformer over 5 different datasets, iii) We perform an extensive factor analysis on the Hyperspectral transformer search performance, greatly motivating future research in this direction. All benchmark materials are available at HyTAS.<|reference_end|>
@article{zhou2024hytas:,
    title={HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark
  and Analysis},
    author={Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren, and Ran Piao},
    journal={arXiv preprint arXiv:2407.16269},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16269},
    primaryClass={cs.CV}
}