hide
Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
Enhancing the robustness of vision algorithms in real-world scenarios is
challenging. One reason is that existing robustness benchmarks are limited, as
they either rely on synthetic data or ignore the effects of individual nuisance
factors. We introduce OOD-CV-v2, a benchmark dataset that includes
out-of-distribution examples of 10 object categories in terms of pose, shape,
texture, context and the weather conditions, and enables benchmarking of models
for image classification, object detection, and 3D pose estimation. In addition
to this novel dataset, we contribute extensive experiments using popular
baseline methods, which reveal that: 1) Some nuisance factors have a much
stronger negative effect on the performance compared to others, also depending
on the vision task. 2) Current approaches to enhance robustness have only
marginal effects, and can even reduce robustness. 3) We do not observe
significant differences between convolutional and transformer architectures. We
believe our dataset provides a rich test bed to study robustness and will help
push forward research in this area.
Our dataset can be accessed from http://www.ood-cv.org/challenge.html
