Vision Checklist：面向图像模型的可测试错误分析，以帮助系统设计人员询问模型能力
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
摘要：
由于最近的模型（如视觉Transformer和其他基于CNN的模型，如VGG和Resnet）的成功，使用大型预训练模型进行图像识别任务变得越来越普遍。这些模型在基准任务上的高准确性已经转化为它们在许多领域的实际应用，包括自动驾驶和医疗诊断等安全关键型应用。尽管它们被广泛使用，但图像模型已被证明对操作环境的变化很脆弱，使其鲁棒性受到质疑。有一个迫切需要的方法，系统地验证和量化这些模型的能力，以帮助设计人员了解和提供有关其安全性和鲁棒性的保证。在本文中，我们提出了视觉检查表，一个框架，旨在询问一个模型的能力，以产生一个报告，可用于系统设计师的鲁棒性评估。该框架提出了一组可以应用于底层数据的扰动操作，以生成不同类型的测试样本。扰动反映了操作环境中的潜在变化，并询问从严格定量到更定性的各种属性。我们的框架在多个数据集上进行了评估，如Tinyimagenet，CIFAR10，CIFAR100和Camelyon17，以及ViT和Resnet等模型。我们的愿景清单提出了一套具体的评估，可以集成到先前提出的概念模型卡。像我们的检查表这样的鲁棒性评估在未来的视觉感知模块安全评估中至关重要，并且对于参与这些系统认证的设计师，部署者和监管机构等广泛的利益相关者都很有用。愿景清单的源代码将开放供公众使用。
英文摘要：
Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and other CNN-based models like VGG and Resnet. The high accuracy of these models on benchmark tasks has translated into their practical use across many domains including safety-critical applications like autonomous driving and medical diagnostics. Despite their widespread use, image models have been shown to be fragile to changes in the operating environment, bringing their robustness into question. There is an urgent need for methods that systematically characterise and quantify the capabilities of these models to help designers understand and provide guarantees about their safety and robustness. In this paper, we propose Vision Checklist, a framework aimed at interrogating the capabilities of a model in order to produce a report that can be used by a system designer for robustness evaluations. This framework proposes a set of perturbation operations that can be applied on the underlying data to generate test samples of different types. The perturbations reflect potential changes in operating environments, and interrogate various properties ranging from the strictly quantitative to more qualitative. Our framework is evaluated on multiple datasets like Tinyimagenet, CIFAR10, CIFAR100 and Camelyon17 and for models like ViT and Resnet. Our Vision Checklist proposes a specific set of evaluations that can be integrated into the previously proposed concept of a model card. Robustness evaluations like our checklist will be crucial in future safety evaluations of visual perception modules, and be useful for a wide range of stakeholders including designers, deployers, and regulators involved in the certification of these systems. Source code of Vision Checklist would be open for public use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
