FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912640621608960 |
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| author | Camuffo, Elena Michieli, Umberto Moon, Jijoong Kim, Daehyun Ozay, Mete |
| author_facet | Camuffo, Elena Michieli, Umberto Moon, Jijoong Kim, Daehyun Ozay, Mete |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14335 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images Camuffo, Elena Michieli, Umberto Moon, Jijoong Kim, Daehyun Ozay, Mete Computer Vision and Pattern Recognition 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. |
| title | FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.14335 |