Category-Adaptive Domain Adaptation for Semantic Segmentation
This work addresses domain adaptation challenges in semantic segmentation, offering incremental improvements for applications like autonomous driving.
The paper tackles unsupervised domain adaptation for semantic segmentation by introducing adversarial learning to bridge style gaps and a category-adaptive threshold for pseudo-label selection, achieving state-of-the-art performance with noticeable gains on GTA5 to Cityscapes adaptation.
Unsupervised domain adaptation (UDA) becomes more and more popular in tackling real-world problems without ground truth of the target domain. Though tedious annotation work is not required, UDA unavoidably faces two problems: 1) how to narrow the domain discrepancy to boost the transferring performance; 2) how to improve pseudo annotation producing mechanism for self-supervised learning (SSL). In this paper, we focus on UDA for semantic segmentation task. Firstly, we introduce adversarial learning into style gap bridging mechanism to keep the style information from two domains in the similar space. Secondly, to keep the balance of pseudo labels on each category, we propose a category-adaptive threshold mechanism to choose category-wise pseudo labels for SSL. The experiments are conducted using GTA5 as the source domain, Cityscapes as the target domain. The results show that our model outperforms the state-of-the-arts with a noticeable gain on cross-domain adaptation tasks.