Importance of visual context in scene understanding tasks is well recognized in the computer vision community. However, to what extent the computer vision models for image classification and semantic segmentation are dependent on the context to make their predictions is unclear. A model overly relying on context will fail when encountering objects in context distributions different from training data and hence it is important to identify these dependencies before we can deploy the models in the real-world. We propose a method to quantify the sensitivity of black-box vision models to visual context by editing images to remove selected objects and measuring the response of the target models. We apply this methodology on two tasks, image classification and semantic segmentation, and discover undesirable dependency between objects and context, for example that "sidewalk" segmentation relies heavily on "cars" being present in the image. We propose an object removal based data augmentation solution to mitigate this dependency and increase the robustness of classification and segmentation models to contextual variations. Our experiments show that the proposed data augmentation helps these models improve the performance in out-of-context scenarios, while preserving the performance on regular data.

该研究论文中提出了一种量化黑盒视觉模型对视觉背景依赖性的方法，通过编辑图像并测量目标模型的响应，以发现物体与背景之间的不良依赖性，并提出基于物体去除的数据增强方案以缓解这种依赖性，从而提高分类和分割模型对环境变化的鲁棒性。

不用汽车看路边：量化并控制上下文对分类和分割的影响