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Journal Article
CRFNet: Context ReFinement Network used for semantic segmentation
- Authors
- Taeghyun An, Jungyu Kang, Dooseop Choi, Kyoung-Wook Min
- Issue Date
- 2023-10
- Citation
- ETRI Journal, v.45, no.5, pp.822-835
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- Recent semantic segmentation frameworks usually combine low‐level and high‐level context information to achieve improved performance. In addition, postlevel context information is also considered. In this study, we present a Context ReFinement Network (CRFNet) and its training method to improve the semantic predictions of segmentation models of the encoder–decoder structure. Our study is based on postprocessing, which directly considers the relationship between spatially neighboring pixels of a label map, such as Markov and conditional random fields. CRFNet comprises two modules: a refiner and a combiner that, respectively, refine the context information from the output features of the conventional semantic segmentation network model and combine the refined features with the intermediate features from the decoding process of the segmentation model to produce the final output. To train CRFNet to refine the semantic predictions more accurately, we proposed a sequential training scheme. Using various backbone networks (ENet, ERFNet, and HyperSeg), we extensively evaluated our model on three large‐scale, real‐world datasets to demonstrate the effectiveness of our approach.
- KSP Keywords
- Backbone Network, Conditional Random Field(CRF), Context Information, Improved performance, Network Model, Semantic segmentation, training method, training scheme
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
