LabelBank: Revisiting Global Perspectives for Semantic Segmentation
This work addresses semantic segmentation for computer vision applications, presenting an incremental improvement by integrating global inference with existing methods.
The paper tackles the problem of noisy pixel labels in semantic segmentation by proposing a generic framework called LabelBank that leverages holistic image information to improve accuracy, demonstrating performance gains across various state-of-the-art architectures and datasets.
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.