---
author:
- 'Ruimao Zhang, Liang Lin, Guangrun Wang, Meng Wang, and Wangmeng Zuo'
bibliography:
- 'IEEEtran.bib'
title: Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
---

[Shell : Bare Demo of IEEEtran.cls for IEEE Transactions on Magnetics Journals]{}

Introduction
============

Scene understanding started with the goal of creating systems that can infer meaningful configurations (e.g., parts, objects and their compositions with relations) from imagery like humans [@DBLP:ImageParsing-Attribute][@J:Space-Tiling]. In computer vision research, most of the scene understanding methods focus on semantic scene labeling / segmentation problems (e.g., assigning semantic labels to each pixel) [@DBLP:SemanSeg-1][@DBLP:SemanSeg-3][@DBLP:FCnetwork][@DBLP:RecursiveContext]. Yet relatively few works attempt to explore how to automatically generate a structured and meaningful configuration of the input scene, which is an essential task to human cognition [@B:CognitionScience]. In spite of some acknowledged structured models beyond scene labeling, e.g., and-or graph (AoG) [@J:AoG], factor graph (FG) [@J:FactorGraphs] and recursive neural network (RsNN) [@DBLP:Recursive_Socher], learning the hierarchical scene structure remains a challenge due to the following difficulties.

-   The parsing configurations of nested hierarchical structure in scene images are often ambiguous, e.g., a configuration may have more than one parse. Moreover, making the parsing result in accordance with human perception is also intractable.

-   