Back to Search
Start Over
CAN: Contextual Aggregating Network for Semantic Segmentation
CAN: Contextual Aggregating Network for Semantic Segmentation
- Source :
- ICASSP
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Fully convolutional neural networks (FCNs) have shown great success in dense estimation tasks. One key pillar of such progress is mining multi-scale context cues from features in different convolutional layers. This paper introduces contextual aggregating network(CAN), a generic convolutional feature ensembling framework for semantic segmentation. Our framework first captures multi-scale contextual clues by concatenating multi-level feature representation, which carries both coarse semantics and fine details. Then it adaptively integrates stacked features to perform dense pixel estimation. The proposed CAN is trainable end-to-end, and allows us to fully investigate multi-scale context information embedded in images. The experiments show the promising results of our method on PASCAL VOC 2012 and Cityscapes dataset.
- Subjects :
- Pixel
Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020207 software engineering
Pattern recognition
02 engineering and technology
Pascal (programming language)
Convolutional neural network
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Segmentation
Artificial intelligence
business
computer
computer.programming_language
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Accession number :
- edsair.doi...........aaa6fbf63d4e2f5ff600d2ae6f66e126
- Full Text :
- https://doi.org/10.1109/icassp.2019.8683673