University of Twente Student Theses
CABiNet : Efficient Context Aggregation Network for Low-Latency Semantic Segmentation
Saksena, Saumya (2020) CABiNet : Efficient Context Aggregation Network for Low-Latency Semantic Segmentation.
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| Abstract: | Real-time semantic segmentation is a challenging task as the optimal balance between accuracy and efficiency (computational complexity, memory footprint and execution speed) is hard to achieve. Conventional lightweight and real-time semantic segmentation architectures usually address only one of the above perspectives, thereby making high-accuracy designs computationally expensive and high-speed models relatively inaccurate. In this research, we introduce an approach to semantic segmentation (for images), which successfully reduces the computational costs by almost 88% and increases the execution speed by a factor of 1.5 compared to the current state-of-the-art, while maintaining a comparable mean intersection-over- union score. Building upon the existing multi-branch architectures for high-speed segmentation, we design a cheap high resolution branch for effective spatial detailing and a context branch with compact asymmetric position and local attention (collectively termed as Context Aggregation Block), potent enough to capture both long-range and local contextual dependencies required for accurate semantic segmentation, at low computational costs. Specifically, we achieve 76.6% and 75.8% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on a single NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Our superior context aggregation techniques also outperform the current state-of-the-art on another public benchmark, UAVid dataset, by a significant margin of 14%. Codes and pre-trained models shall be made available at https://github.com/dronefreak/CABiNet. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 31 mathematics, 50 technical science in general, 54 computer science |
| Programme: | Systems and Control MSc (60359) |
| Link to this item: | https://purl.utwente.nl/essays/84370 |
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