PIG: Prompt Images Guidance for Night-Time Scene Parsing

• URL: http://arxiv.org/abs/2406.10531v1
• Date: Sat, 15 Jun 2024 07:06:19 GMT
• Title: PIG: Prompt Images Guidance for Night-Time Scene Parsing
• Authors: Zhifeng Xie, Rui Qiu, Sen Wang, Xin Tan, Yuan Xie, Lizhuang Ma,
• Abstract summary: Unsupervised domain adaptation (UDA) has become the predominant method for studying night scenes. We propose a Night-Focused Network (NFNet) to learn night-specific features from both target domain images and prompt images. We conduct experiments on four night-time datasets: NightCity, NightCity+, Dark Zurich, and ACDC.
• Score: 48.35991796324741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Night-time scene parsing aims to extract pixel-level semantic information in night images, aiding downstream tasks in understanding scene object distribution. Due to limited labeled night image datasets, unsupervised domain adaptation (UDA) has become the predominant method for studying night scenes. UDA typically relies on paired day-night image pairs to guide adaptation, but this approach hampers dataset construction and restricts generalization across night scenes in different datasets. Moreover, UDA, focusing on network architecture and training strategies, faces difficulties in handling classes with few domain similarities. In this paper, we leverage Prompt Images Guidance (PIG) to enhance UDA with supplementary night knowledge. We propose a Night-Focused Network (NFNet) to learn night-specific features from both target domain images and prompt images. To generate high-quality pseudo-labels, we propose Pseudo-label Fusion via Domain Similarity Guidance (FDSG). Classes with fewer domain similarities are predicted by NFNet, which excels in parsing night features, while classes with more domain similarities are predicted by UDA, which has rich labeled semantics. Additionally, we propose two data augmentation strategies: the Prompt Mixture Strategy (PMS) and the Alternate Mask Strategy (AMS), aimed at mitigating the overfitting of the NFNet to a few prompt images. We conduct extensive experiments on four night-time datasets: NightCity, NightCity+, Dark Zurich, and ACDC. The results indicate that utilizing PIG can enhance the parsing accuracy of UDA.

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