Semantic segmentation plays a critical role in enabling intelligent vehicles
to comprehend their surrounding environments. However, deep learning-based
methods usually perform poorly in domain shift scenarios due to the lack of
labeled data for training. Unsupervised domain adaptation (UDA) techniques have
emerged to bridge the gap across different driving scenes and enhance model
performance on unlabeled target environments. Although self-training UDA
methods have achieved state-of-the-art results, the challenge of generating
precise pseudo-labels persists. These pseudo-labels tend to favor majority
classes, consequently sacrificing the performance of rare classes or small
objects like traffic lights and signs. To address this challenge, we introduce
SAM4UDASS, a novel approach that incorporates the Segment Anything Model (SAM)
into self-training UDA methods for refining pseudo-labels. It involves
Semantic-Guided Mask Labeling, which assigns semantic labels to unlabeled SAM
masks using UDA pseudo-labels. Furthermore, we devise fusion strategies aimed
at mitigating semantic granularity inconsistency between SAM masks and the
target domain. SAM4UDASS innovatively integrate SAM with UDA for semantic
segmentation in driving scenes and seamlessly complements existing
self-training UDA methodologies. Extensive experiments on synthetic-to-real and
normal-to-adverse driving datasets demonstrate its effectiveness. It brings
more than 3% mIoU gains on GTA5-to-Cityscapes, SYNTHIA-to-Cityscapes, and
Cityscapes-to-ACDC when using DAFormer and achieves SOTA when using MIC. The
code will be available at https://github.com/ywher/SAM4UDASS.
SAM4UDASS: Enhancing Unsupervised Domain Adaptation for Semantic Segmentation in Driving Scenes
Most smart vehicles rely on semantic segmentation to understand their surroundings, but deep learning methods struggle when faced with different driving scenarios that they haven’t been trained on. Unsupervised domain adaptation (UDA) techniques have emerged as a solution to bridge this gap, allowing models to perform well in new, unlabeled environments. However, one of the major challenges in UDA is generating precise pseudo-labels, which often prioritize majority classes and overlook rare or small objects like traffic lights and signs.
To address this challenge, the researchers propose a novel approach called SAM4UDASS (Segment Anything Model for Unsupervised Domain Adaptation in Semantic Segmentation). This approach integrates the Segment Anything Model (SAM) into self-training UDA methods to refine pseudo-labels. SAM is a framework that allows for semantic-guided mask labeling, assigning semantic labels to unlabeled masks generated by UDA techniques.
The researchers also introduce fusion strategies to mitigate inconsistency in semantic granularity between the SAM masks and the target domain. This ensures that the refined pseudo-labels accurately represent the objects and classes present in the target environment. By seamlessly integrating SAM with UDA, SAM4UDASS complements existing self-training UDA methodologies and addresses the limitations of previous approaches.
To evaluate the effectiveness of SAM4UDASS, extensive experiments were conducted on synthetic-to-real and normal-to-adverse driving datasets. The results showed that SAM4UDASS outperforms state-of-the-art methods like DAFormer and MIC, achieving more than 3% mIoU gains on challenging domain adaptation tasks like GTA5-to-Cityscapes, SYNTHIA-to-Cityscapes, and Cityscapes-to-ACDC.
The availability of the code on GitHub (https://github.com/ywher/SAM4UDASS) allows other researchers and practitioners to further explore and utilize SAM4UDASS in their own projects, contributing to the advancement of unsupervised domain adaptation for semantic segmentation in driving scenes.
Multi-disciplinary Nature of SAM4UDASS
SAM4UDASS is a prime example of the multi-disciplinary nature of the field it operates in. It combines concepts from computer vision, deep learning, and domain adaptation to tackle the challenges of semantic segmentation in driving scenes. By integrating the Segment Anything Model (SAM), which is a tool designed for flexible mask annotation and labeling, SAM4UDASS bridges the gap between semantic segmentation and unsupervised domain adaptation.
The fusion strategies introduced in SAM4UDASS highlight the importance of considering the semantic granularity of masks in order to achieve accurate segmentation results. This not only requires expertise in computer vision algorithms, but also an understanding of the specific requirements and challenges posed by driving scenes.
Furthermore, SAM4UDASS demonstrates the significance of open-source development and collaboration within the research community. By making their code available on GitHub, the researchers encourage others to build upon their work and contribute to the ongoing progress in unsupervised domain adaptation for intelligent vehicles.
Expert Insight: SAM4UDASS represents a valuable contribution to the field of intelligent transportation systems. By addressing the limitations of existing self-training UDA methods and enhancing the accuracy of pseudo-labels, SAM4UDASS brings us closer to achieving reliable and robust semantic segmentation in various driving scenarios. Its successful application in synthetic-to-real and normal-to-adverse domain adaptation tasks demonstrates its potential for real-time implementation in autonomous vehicles.