OOD-CV-v2 : An Extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images

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 o...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 46; no. 12; pp. 11104 - 11118
Main Authors Zhao, Bingchen, Wang, Jiahao, Ma, Wufei, Jesslen, Artur, Yang, Siwei, Yu, Shaozuo, Zendel, Oliver, Theobalt, Christian, Yuille, Alan L., Kortylewski, Adam
Format Journal Article
LanguageEnglish
Published United States IEEE 01.12.2024
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Summary: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.
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ISSN:0162-8828
1939-3539
1939-3539
2160-9292
DOI:10.1109/TPAMI.2024.3462293