Night-time Scene Parsing with a Large Real Dataset

• URL: http://arxiv.org/abs/2003.06883v3
• Date: Fri, 1 Apr 2022 05:40:06 GMT
• Title: Night-time Scene Parsing with a Large Real Dataset
• Authors: Xin Tan and Ke Xu and Ying Cao and Yiheng Zhang and Lizhuang Ma and Rynson W.H. Lau
• Abstract summary: We aim to address the night-time scene parsing (NTSP) problem, which has two main challenges. To tackle the scarcity of night-time data, we collect a novel labeled dataset, named it NightCity, of 4,297 real night-time images. We also propose an exposure-aware framework to address the NTSP problem through augmenting the segmentation process with explicitly learned exposure features.
• Score: 67.11211537439152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although huge progress has been made on scene analysis in recent years, most existing works assume the input images to be in day-time with good lighting conditions. In this work, we aim to address the night-time scene parsing (NTSP) problem, which has two main challenges: 1) labeled night-time data are scarce, and 2) over- and under-exposures may co-occur in the input night-time images and are not explicitly modeled in existing pipelines. To tackle the scarcity of night-time data, we collect a novel labeled dataset, named {\it NightCity}, of 4,297 real night-time images with ground truth pixel-level semantic annotations. To our knowledge, NightCity is the largest dataset for NTSP. In addition, we also propose an exposure-aware framework to address the NTSP problem through augmenting the segmentation process with explicitly learned exposure features. Extensive experiments show that training on NightCity can significantly improve NTSP performances and that our exposure-aware model outperforms the state-of-the-art methods, yielding top performances on our dataset as well as existing datasets.

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