Ordinal Multi-task Part Segmentation with Recurrent Prior Generation.

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ID: 96733
2019
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Abstract
Semantic object part segmentation is a fundamental task in object understanding and geometric analysis. The clear understanding of part relationships can be of great use to the segmentation process. In this work, we propose a novel Ordinal Multi-task Part Segmentation (OMPS) approach which explicitly models the part ordinal relationship to guide the segmentation process in a recurrent manner. Quantitative and qualitative experiments are conducted first to explore the mutual impacts among object parts and then an ordinal part inference algorithm is formulated via experimental observations. Specifically, our framework is mainly composed of two modules, the forward module to segment multiple parts as individual subtasks with prior knowledge, and the recurrent module to generate appropriate part priors with the ordinal inference algorithm. These two modules work iteratively to optimize the segmentation performance and the network parameters. Experimental results show that our approach outperforms the state-of-the-art models on human and vehicle part parsing benchmarks. Comprehensive evaluations are conducted to demonstrate the effectiveness of our approach in object part segmentation.
Reference Key
zhao2019ordinalieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhao, Yifan;Li, Jia;Zhang, Yu;Song, Yafei;Tian, Yonghong;
Journal ieee transactions on pattern analysis and machine intelligence
Year 2019
DOI
10.1109/TPAMI.2019.2953854
URL
Keywords
semantic uncertainty evidential occupancy grid lane grid prior map

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