LabelBank: Revisiting Global Perspectives for Semantic Segmentation
arXiv:1703.09891
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.
Pre-prints
References in corpus (12)
- Deep Residual Learning for Image Recognition
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- Multi-Scale Context Aggregation by Dilated Convolutions
- Going Deeper with Convolutions
- ParseNet: Looking Wider to See Better
- Convolutional Feature Masking for Joint Object and Stuff Segmentation
- BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
- Instance-aware Semantic Segmentation via Multi-task Network Cascades
- Contextual Action Recognition with R*CNN
- COCO-Stuff: Thing and Stuff Classes in Context
- Learning Structured Inference Neural Networks with Label Relations