Semantic Object Parsing with Local-Global Long Short-Term Memory
Xiaodan Liang, Xiaohui Shen, Donglai Xiang, Jiashi Feng and, Liang Lin, Shuicheng Yan

TL;DR
This paper introduces a novel LG-LSTM architecture that effectively integrates local and global contextual information for pixel-level semantic object parsing, significantly improving accuracy over previous methods.
Contribution
The work presents a new deep LG-LSTM model that captures multi-scale spatial dependencies and enhances feature learning for semantic parsing in an end-to-end manner.
Findings
LG-LSTM outperforms state-of-the-art methods on three datasets.
Stacked LG-LSTM layers improve contextual feature integration.
End-to-end training enhances parsing accuracy.
Abstract
Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical for achieving such fine-grained pixel-level recognition. Prior methods often leverage the contextual information through post-processing predicted confidence maps. In this work, we propose a novel deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions. In each LG-LSTM layer, local guidance from neighboring positions and global guidance from the whole image are imposed on each position to better exploit complex local and global contextual information. Individual LSTMs for distinct spatial dimensions are also utilized to intrinsically capture various spatial layouts of…
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Videos
Semantic Object Parsing With Local-Global Long Short-Term Memory· youtube
Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques · Advanced Neural Network Applications
