We study the robustness of image classifiers to temporal perturbations derived from videos. As part of this study, we construct two datasets, ImageNet-Vid-Robust and YTBB-Robust , containing a total 57,897 images grouped into 3,139 sets of perceptually similar images. Our datasets were derived from ImageNet-Vid and Youtube-BB respectively and thoroughly re-annotated by human experts for image similarity. We evaluate a diverse array of classifiers pre-trained on ImageNet and show a median classification accuracy drop of 16 and 10 on our two datasets. Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points. Our analysis demonstrates that perturbations occurring naturally in videos pose a substantial and realistic challenge to deploying convolutional neural networks in environments that require both reliable and low-latency predictions
@article{arxiv.1906.02168,
title = {Do Image Classifiers Generalize Across Time?},
author = {Vaishaal Shankar and Achal Dave and Rebecca Roelofs and Deva Ramanan and Benjamin Recht and Ludwig Schmidt},
journal= {arXiv preprint arXiv:1906.02168},
year = {2019}
}
Comments
23 pages, 11 tables, 11 figures. Paper Website: https://modestyachts.github.io/natural-perturbations-website/