# LabelBank: Revisiting Global Perspectives for Semantic Segmentation Source: [LabelBank: Revisiting Global Perspectives for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:36:15.201Z Authors: Hexiang Hu, Zhiwei Deng, Guang-Tong Zhou, Fei Sha, Greg Mori Published: 2017-03-29T05:58:21.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Coco-Stuff (test)](): Semantic segmentation; Coco-Stuff (test); mIoU: 34.3. ## Original sources and implementation [Original paper]() [PDF]()