Fully Connected Deep Structured Networks

Abstract

Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and more recently also semantic segmentation. Particularly for semantic segmentation, a two-stage procedure is often employed. Hereby, convolutional networks are trained to provide good local pixel-wise features for the second step being traditionally a more global graphical model. In this work we unify this two-stage process into a single joint training algorithm. We demonstrate our method on the semantic image segmentation task and show encouraging results on the challenging PASCAL VOC 2012 dataset.

References in corpus (6)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Natural Language Processing (almost) from Scratch
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
• Show and Tell: A Neural Image Caption Generator
• Deep Visual-Semantic Alignments for Generating Image Descriptions

Cited by in corpus (4)

• Adversarial Networks for the Detection of Aggressive Prostate Cancer
• Learning Deep Structured Models
• Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
• Progressively Diffused Networks for Semantic Image Segmentation