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Semantic-spatial fusion network for human parsing.
- Source :
-
Neurocomputing . Aug2020, Vol. 402, p375-383. 9p. - Publication Year :
- 2020
-
Abstract
- Recently, many methods have united low-level and high-level features to generate the desired accurate high-resolution prediction for human parsing. Nevertheless, there exists a semantic-spatial gap between low-level and high-level features in some methods, i.e., high-level features represent more semantics and less spatial details, while low-level ones have less semantics and more spatial details. In this paper, we propose a Semantic-Spatial Fusion Network (SSFNet) for human parsing to shrink the gap, which generates the accurate high-resolution prediction by aggregating multi-resolution features. SSFNet includes two models, a semantic modulation model and a resolution-aware model. The semantic modulation model guides spatial details with semantics and then effectively facilitates the feature fusion, narrowing the gap. The resolution-aware model sufficiently boosts the feature fusion and obtains multi-receptive-fields, which generates reliable and fine-grained high-resolution features for each branch, in bottom-up and top-down processes. Extensive experiments on three public datasets, PASCAL-Person-Part, LIP and PPSS, show that SSFNet achieves significant improvements over state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FORECASTING
*DATA fusion (Statistics)
*SEMANTICS
*LIPS
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 402
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 143463025
- Full Text :
- https://doi.org/10.1016/j.neucom.2020.03.096