AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic
  Segmentation
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
6883c1b27d7a19a208cdef24
Md. Al-Masrur Khan
claimed_verified
2025-07-28T08:45:27.007Z
6883c09516f1ec82fa56f2a7
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/pozHaETGZj-a8bITgG78r.png
Md. Al-Masrur Khan
user
Masrur02
6883c1b27d7a19a208cdef25
Durgakant Pushp
6883c1b27d7a19a208cdef26
Lantao Liu
AFRDA: Attentive Feature Refinement for Domain Adaptive Semantic
  Segmentation
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
The Adaptive Feature Refinement (AFR) module enhances unsupervised domain adaptive semantic segmentation by refining high-resolution features with low-resolution logits and integrating high-frequency components, leading to improved segmentation accuracy.
Unsupervised Domain Adaptive Semantic Segmentation UDA-SS Adaptive Feature Refinement AFR semantic priors low-resolution logits high-frequency components uncertainty-driven attention HRDA-based UDA methods mIoU GTA V Cityscapes Synthia