Semantic segmentation requires a detailed labeling of image pixels by object category. Information derived from local image patches is necessary to describe the detailed shape of individual objects. However, this information is ambiguous and can result in noisy labels. Global inference of image content can instead capture the general semantic concepts present. We advocate that holistic inference of image concepts provides valuable information for detailed pixel labeling. We propose a generic framework to leverage holistic information in the form of a LabelBank for pixel-level segmentation. We show the ability of our framework to improve semantic segmentation performance in a variety of settings. We learn models for extracting a holistic LabelBank from visual cues, attributes, and/or textual descriptions. We demonstrate improvements in semantic segmentation accuracy on standard datasets across a range of state-of-the-art segmentation architectures and holistic inference approaches.

提出基于全局推断的标签库（LabelBank）框架，通过视觉提示、属性和/或文本描述来提取全局概念，以改善语义分割性能。在各种设置下，用该框架得到的LabelBank，相比于局部信息，可以更好地描述对象的详细形状，进而提高语义分割的准确性。

LabelBank: 重新审视语义分割的全局视角