# LabelBank: Revisiting Global Perspectives for Semantic Segmentation

arXiv: https://arxiv.org/abs/1703.09891 · HTML: https://mlsanity.com/paper/1703.09891/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2017.

## Built on by (most cited first, 2 of 2)

- [Improving Scene Graph Classification by Exploiting Knowledge from Texts](https://mlsanity.com/paper/2102.04760/) (2021)
- [ORDNet: Capturing Omni-Range Dependencies for Scene Parsing](https://mlsanity.com/paper/2101.03929/) (2021)

## Abstract (arXiv)

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.