# AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation Source: [AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:21:01.557Z Authors: Md. Al-Masrur Khan, Durgakant Pushp, Lantao Liu Published: 2025-07-23T22:02:17.000Z ## Abstract In Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS), a model is trained on labeled source domain data (e.g., synthetic images) and adapted to an unlabeled target domain (e.g., real-world images) without access to target annotations. Existing UDA-SS methods often struggle to balance fine-grained local details with global contextual information, leading to segmentation errors in complex regions. To address this, we introduce the Adaptive Feature Refinement (AFR) module, which enhances segmentation accuracy by refining highresolution features using semantic priors from low-resolution logits. AFR also integrates high-frequency components, which capture fine-grained structures and provide crucial boundary information, improving object delineation. Additionally, AFR adaptively balances local and global information through uncertaintydriven attention, reducing misclassifications. Its lightweight design allows seamless integration into HRDA-based UDA methods, leading to state-of-the-art segmentation performance. Our approach improves existing UDA-SS methods by 1.05% mIoU on GTA V --\> Cityscapes and 1.04% mIoU on Synthia--\>Cityscapes. The implementation of our framework is available at: https://github.com/Masrur02/AFRDA ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()