FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images

• URL: http://arxiv.org/abs/2403.14335v1
• Date: Thu, 21 Mar 2024 12:01:54 GMT
• Title: FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images
• Authors: Elena Camuffo, Umberto Michieli, Jijoong Moon, Daehyun Kim, Mete Ozay,
• Abstract summary: 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.
• Score: 19.07004663565609
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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