Exploring Reliable Matching with Phase Enhancement for Night-time Semantic Segmentation

• URL: http://arxiv.org/abs/2408.13838v1
• Date: Sun, 25 Aug 2024 13:59:31 GMT
• Title: Exploring Reliable Matching with Phase Enhancement for Night-time Semantic Segmentation
• Authors: Yuwen Pan, Rui Sun, Naisong Luo, Tianzhu Zhang, Yongdong Zhang,
• Abstract summary: We propose a novel end-to-end optimized approach, named NightFormer, tailored for night-time semantic segmentation. Specifically, we design a pixel-level texture enhancement module to acquire texture-aware features hierarchically with phase enhancement and amplified attention. Our proposed method performs favorably against state-of-the-art night-time semantic segmentation methods.
• Score: 58.180226179087086
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic segmentation of night-time images holds significant importance in computer vision, particularly for applications like night environment perception in autonomous driving systems. However, existing methods tend to parse night-time images from a day-time perspective, leaving the inherent challenges in low-light conditions (such as compromised texture and deceiving matching errors) unexplored. To address these issues, we propose a novel end-to-end optimized approach, named NightFormer, tailored for night-time semantic segmentation, avoiding the conventional practice of forcibly fitting night-time images into day-time distributions. Specifically, we design a pixel-level texture enhancement module to acquire texture-aware features hierarchically with phase enhancement and amplified attention, and an object-level reliable matching module to realize accurate association matching via reliable attention in low-light environments. Extensive experimental results on various challenging benchmarks including NightCity, BDD and Cityscapes demonstrate that our proposed method performs favorably against state-of-the-art night-time semantic segmentation methods.

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