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

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.

Related papers

Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics [22.876222327262596]
We introduce Per-corruption Adaptation of Normalization statistics (PAN) to enhance the model robustness of vision systems. Our approach entails three key components: (i) a corruption type identification module, (ii) dynamic adjustment of normalization layer statistics based on identified corruption type, and (iii) real-time update of these statistics according to input data.
arXiv Detail & Related papers (2024-07-08T23:20:18Z)
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild [54.139923409101044]
We propose a novel IQA method called diffusion priors-based IQA (DP-IQA) We use pre-trained stable diffusion as the backbone, extract multi-level features from the denoising U-Net, and decode them to estimate the image quality score. We distill the knowledge in the above model into a CNN-based student model, significantly reducing the parameter to enhance applicability.
arXiv Detail & Related papers (2024-05-30T12:32:35Z)
Dynamic Batch Norm Statistics Update for Natural Robustness [5.366500153474747]
We propose a unified framework consisting of a corruption-detection model and BN statistics update. Our results demonstrate about 8% and 4% accuracy improvement on CIFAR10-C and ImageNet-C.
arXiv Detail & Related papers (2023-10-31T17:20:30Z)
Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions [48.34142457385199]
We present MUFIA, an algorithm designed to identify the specific types of corruptions that can cause models to fail. We find that even state-of-the-art models trained to be robust against known common corruptions struggle against the low visibility-based corruptions crafted by MUFIA.
arXiv Detail & Related papers (2023-06-12T15:19:13Z)
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions [58.306694836881235]
We present Robo3D, the first comprehensive benchmark heading toward probing the robustness of 3D detectors and segmentors under out-of-distribution scenarios. We consider eight corruption types stemming from severe weather conditions, external disturbances, and internal sensor failure. We propose a density-insensitive training framework along with a simple flexible voxelization strategy to enhance the model resiliency.
arXiv Detail & Related papers (2023-03-30T17:59:17Z)
Benchmarking Robustness in Neural Radiance Fields [22.631924719238963]
We analyze the robustness of NeRF-based novel view synthesis algorithms in the presence of different types of corruptions. We find that NeRF-based models are significantly degraded in the presence of corruption, and are more sensitive to a different set of corruptions than image recognition models.
arXiv Detail & Related papers (2023-01-10T17:01:12Z)
Improving robustness against common corruptions with frequency biased models [112.65717928060195]
unseen image corruptions can cause a surprisingly large drop in performance. Image corruption types have different characteristics in the frequency spectrum and would benefit from a targeted type of data augmentation. We propose a new regularization scheme that minimizes the total variation (TV) of convolution feature-maps to increase high-frequency robustness.
arXiv Detail & Related papers (2021-03-30T10:44:50Z)
Revisiting Batch Normalization for Improving Corruption Robustness [85.20742045853738]
We interpret corruption robustness as a domain shift and propose to rectify batch normalization statistics for improving model robustness. We find that simply estimating and adapting the BN statistics on a few representation samples, without retraining the model, improves the corruption robustness by a large margin.
arXiv Detail & Related papers (2020-10-07T19:56:47Z)
Inducing Predictive Uncertainty Estimation for Face Recognition [102.58180557181643]
We propose a method for generating image quality training data automatically from'mated-pairs' of face images. We use the generated data to train a lightweight Predictive Confidence Network, termed as PCNet, for estimating the confidence score of a face image.
arXiv Detail & Related papers (2020-09-01T17:52:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.