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, 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 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 testbed to study robustness and will help
push forward research in this area.

本文引入 OOD-CV 数据集，并发现某些干扰因素有更强烈的负面影响；当前的提高鲁棒性的方法只有微弱的效果，甚至可能削弱鲁棒性；我们的数据集提供了一个丰富的测试床，以研究鲁棒性，并有助于推进这一领域的研究。

OOD-CV: 自然图像个体干扰鲁棒性的离群分布偏移基准

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

In this paper, we present a Nuisance-label Supervision (NLS) module, which
can make models more robust to nuisance factor variations. Nuisance factors are
those irrelevant to a task, and an ideal model should be invariant to them. For
example, an activity recognition model should perform consistently regardless
of the change of clothes and background. But our experiments show existing
models are far from this capability. So we explicitly supervise a model with
nuisance labels to make extracted features less dependent on nuisance factors.
Although the values of nuisance factors are rarely annotated, we demonstrate
that besides existing annotations, nuisance labels can be acquired freely from
data augmentation and synthetic data. Experiments show consistent improvement
in robustness towards image corruption and appearance change in action
recognition.

本研究提出了一种 “Nuisance-label Supervision (NLS)” 模型，旨在使模型更加稳健来应对干扰因素变化，数据实验表明，通过显式监督模型，可以使所提取的特征不再依赖于干扰因素，并通过数据增强和合成数据等方式获得干扰标签，对于图像破损和外观改变的动作识别具有一致的改善稳健性能。

通过免费标签提高鲁棒性的干扰标签监督

Nuisance-Label Supervision: Robustness Improvement by Free Labels

Supervised machine learning models often associate irrelevant nuisance
factors with the prediction target, which hurts generalization. We propose a
framework for training robust neural networks that induces invariance to
nuisances through learning to discover and separate predictive and nuisance
factors of data. We present an information theoretic formulation of our
approach, from which we derive training objectives and its connections with
previous methods. Empirical results on a wide array of datasets show that the
proposed framework achieves state-of-the-art performance, without requiring
nuisance annotations during training.

提出了一种用于训练神经网络的框架，以识别和区分数据的预测和负面因素，并通过学习消除负面因素，从而实现对负面因素的不变性，从而实现对负面因素的不变性，并通过信息论框架和推导出的训练目标，实现了最先进的性能。