Do Image Classifiers Generalize Across Time?
This addresses the challenge of deploying convolutional neural networks in real-time environments where natural video perturbations cause reliability issues, representing an incremental analysis of robustness.
The study tackled the problem of image classifier robustness to temporal perturbations from videos by constructing two datasets with 57,897 images and showing a median accuracy drop of 16 and 10 points for classifiers, and a 14-point drop in detection mAP for models.
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