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。