Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion

TL;DR

This paper introduces a novel pseudo label diffusion method leveraging spatial and temporal cues in foggy driving scenes to improve unsupervised domain adaptation for semantic segmentation.

Contribution

It proposes the TDo-Dif scheme that densifies pseudo labels using superpixels and optical flows, enhancing model adaptation in foggy conditions.

Findings

Achieves 51.92% and 53.84% mIoU on two foggy datasets.

Outperforms state-of-the-art unsupervised domain adaptation methods.

Demonstrates effectiveness of spatial-temporal pseudo label diffusion.

Abstract

Understanding foggy image sequence in the driving scenes 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, the self-training strategy has been considered 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. Models and data can be found at https://github.com/velor2012/TDo-Dif.