FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images
<|reference_start|>FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images: Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents. Particularly, robust test-time performance is imperative for most of the applications. This paper presents a novel approach to improve robustness of any classification model, especially on severely corrupted images. Our method (FROST) employs high-frequency features to detect input image corruption type, and select layer-wise feature normalization statistics. FROST provides the state-of-the-art results for different models and datasets, outperforming competitors on ImageNet-C by up to 37.1% relative gain, improving baseline of 40.9% mCE on severe corruptions.<|reference_end|>
@article{camuffo2024fft-based,
    title={FFT-based Selection and Optimization of Statistics for Robust
  Recognition of Severely Corrupted Images},
    author={Elena Camuffo, Umberto Michieli, Jijoong Moon, Daehyun Kim, Mete Ozay},
    journal={International Conference on Acoustics, Speech, and Signal
  Processing (ICASSP), 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14335},
    primaryClass={cs.CV}
}