@inproceedings{zhou2019context-reinforced,
author = {Zhou, Yizhou and Sun, Xiaoyan and Zha, Zheng-Jun and Zeng, Wenjun},
title = {Context-Reinforced Semantic Segmentation},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2019},
month = {June},
abstract = {Recent efforts have shown the importance of context on deep convolutional neural network based semantic segmentation. Among others, the predicted segmentation map (pmap) itself which encodes rich high-level semantic cues (e.g. objects and layout) can be regarded as a promising source of context. In this paper, we propose a dedicated module, Context Net, to better explore the context information in p-maps. Without introducing any new supervisions, we formulate the context learning problem as a Markov Decision Process and optimize it using reinforcement learning during which the p-map and Context Net are treated as environment and agent, respectively. Through adequate explorations, the Context Net selects the information which has long-term benefit for segmentation inference. By incorporating the Context Net with a baseline segmentation scheme, we then propose a Context-reinforced Semantic Segmentation network (CiSS-Net), which is fully end-to-end trainable. Experimental results show that the learned context brings 3.9% absolute improvement on mIoU over the baseline segmentation method, and the CiSS-Net achieves the state-of-the-art segmentation performance on ADE20K, PASCAL-Context and Cityscapes.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/context-reinforced-semantic-segmentation/},
}