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

Bingchen Zhao;Jiahao Wang;Wufei Ma;Artur Jesslen;Siwei Yang;Shaozuo Yu;Oliver Zendel;Christian Theobalt;Alan L. Yuille;Adam Kortylewski
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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.
OOD-CV-v2:自然图像中单个干扰的分布外偏移鲁棒性扩展基准
提高视觉算法在真实世界场景中的鲁棒性具有挑战性。原因之一是现有的鲁棒性基准有限,因为它们要么依赖于合成数据,要么忽略了个别干扰因素的影响。我们引入了 OOD-CV-v2,这是一个基准数据集,其中包括 10 个物体类别在姿态、形状、纹理、上下文和天气条件方面的分布外示例,可以对图像分类、物体检测和三维姿态估计模型进行基准测试。除了这个新颖的数据集之外,我们还使用流行的基线方法进行了大量实验,结果表明1) 与其他方法相比,某些干扰因素对性能的负面影响更大,这也取决于视觉任务。2) 目前增强鲁棒性的方法效果甚微,甚至会降低鲁棒性。3) 我们没有观察到卷积架构和变压器架构之间的显著差异。我们相信,我们的数据集为研究鲁棒性提供了丰富的测试平台,并将有助于推动该领域的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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