TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency

• URL: http://arxiv.org/abs/2210.05152v5
• Date: Wed, 30 Aug 2023 14:24:46 GMT
• Title: TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency
• Authors: Dan Zhang, Rui Zheng, Luosang Gadeng, Pei Yang
• Abstract summary: This paper addresses the task of semantic segmentation in computer vision, aiming to achieve precise pixel-wise classification. 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.
• Score: 10.92477003580794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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