Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion

• Published in: IEEE transactions on image processing Vol. 31; pp. 3525 - 3540
• Main Authors: Liao, Liang, Chen, Wenyi, Xiao, Jing, Wang, Zheng, Lin, Chia-Wen, Satoh, Shin'ichi
• Format: Journal Article
• Language: English
• Published: United States IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Atmospheric modeling; Data models; Domains; Image Processing, Computer-Assisted - methods; Image segmentation; Label diffusion; Labels; Meteorology; natural foggy scene; Optical flow (image analysis); Scene analysis; self-training; semantic segmentation; Semantics; Similarity; Training; unsupervised domain adaptation; Weather
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2022.3172208

Understanding foggy image sequence in driving scene is critical for autonomous driving, but it remains a challenging task due to the difficulty in collecting and annotating real-world images of adverse weather. Recently, self-training strategy has been considered as a powerful solution for unsupervised domain adaptation, which iteratively adapts the model from the source domain to the target domain by generating target pseudo labels and re-training the model. However, the selection of confident pseudo labels inevitably suffers from the conflict between sparsity and accuracy, both of which will lead to suboptimal models. To tackle this problem, we exploit the characteristics of the foggy image sequence of driving scenes to densify the confident pseudo labels. Specifically, based on the two discoveries of local spatial similarity and adjacent temporal correspondence of the sequential image data, we propose a novel Target-Domain driven pseudo label Diffusion (TDo-Dif) scheme. It employs superpixels and optical flows to identify the spatial similarity and temporal correspondence, respectively, and then diffuses the confident but sparse pseudo labels within a superpixel or a temporal corresponding pair linked by the flow. Moreover, to ensure the feature similarity of the diffused pixels, we introduce local spatial similarity loss and temporal contrastive loss in the model re-training stage. Experimental results show that our TDo-Dif scheme helps the adaptive model achieve 51.92% and 53.84% mean intersection-over-union (mIoU) on two publicly available natural foggy datasets (Foggy Zurich and Foggy Driving), which exceeds the state-of-the-art unsupervised domain adaptive semantic segmentation methods. The proposed method can also be applied to non-sequential images in the target domain by considering only spatial similarity.