classic paper for a introduction to CRF for relational learning

Most state-of-the-art techniques for multi-class image segmentation and labeling use conditional random fields defined over pixels or image regions. While regionlevel models often feature dense pairwise connectivity, pixel-level models are considera

State-of-the-art sequence labeling systems traditionally require large mounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.In this paper, we introduce a novel neu-tral network architecture that benefits fro

一些深度学习论文；senet ,east,pixel-anchor,face-detection 等；senet ,east,pixel-anchor,face-detection 等；senet ,east,pixel-anchor,face-detection 等；senet ,east,pixel-anchor,face-detection 等；senet ,east,pixel-anchor,face-detection 等；senet ,east,pixel-anchor,face-d

In this paper, we propose a robust framework for building extraction in visible band images. We first get an initial classification of the pixels based on an unsupervised presegmentation. Then, we develop a novel conditional random field (CRF) formul