"Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide ..."

Masakazu Yoshimura et al. (2022)

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DOI: 10.48550/ARXIV.2210.16046

access: open

type: Informal or Other Publication

metadata version: 2024-11-12

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  - journals/corr/abs-2210-16046
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  - https://dblp.org/rec/journals/corr/abs-2210-16046
Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, Takeshi Ohashi:
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments. CoRR abs/2210.16046 (2022)

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