TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency
Abstract
This paper addresses the task of semantic segmentation in computer vision, aiming to achieve precise pixel-wise classification. We investigate the joint training of models for semantic edge detection and semantic segmentation, which has shown promise. However, implicit cross-task consistency learning in multi-task networks is limited. To address this, we propose a novel "decoupled cross-task consistency loss" that explicitly enhances cross-task consistency. Our semantic segmentation network, TriangleNet, achieves a substantial 2.88\% improvement over the Baseline in mean Intersection over Union (mIoU) on the Cityscapes test set. Notably, TriangleNet operates at 77.4\% mIoU/46.2 FPS on Cityscapes, showcasing real-time inference capabilities at full resolution. With multi-scale inference, performance is further enhanced to 77.8\%. Furthermore, TriangleNet consistently outperforms the Baseline on the FloodNet dataset, demonstrating its robust generalization capabilities. The proposed method underscores the significance of multi-task learning and explicit cross-task consistency enhancement for advancing semantic segmentation and highlights the potential of multitasking in real-time semantic segmentation.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2022
- DOI:
- 10.48550/arXiv.2210.05152
- arXiv:
- arXiv:2210.05152
- Bibcode:
- 2022arXiv221005152Z
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Accepted for publication in the journal "International Journal of Intelligent Systems"