DADA: Depth-aware Domain Adaptation in Semantic Segmentation (1904.01886v3)

Abstract: Unsupervised domain adaptation (UDA) is important for applications where large scale annotation of representative data is challenging. For semantic segmentation in particular, it helps deploy on real "target domain" data models that are trained on annotated images from a different "source domain", notably a virtual environment. To this end, most previous works consider semantic segmentation as the only mode of supervision for source domain data, while ignoring other, possibly available, information like depth. In this work, we aim at exploiting at best such a privileged information while training the UDA model. We propose a unified depth-aware UDA framework that leverages in several complementary ways the knowledge of dense depth in the source domain. As a result, the performance of the trained semantic segmentation model on the target domain is boosted. Our novel approach indeed achieves state-of-the-art performance on different challenging synthetic-2-real benchmarks.

Depth-Aware Domain Adaptation in Semantic Segmentation

The paper introduces a novel approach termed Depth-Aware Domain Adaptation (DADA) aimed at enhancing semantic segmentation tasks. Specifically, this approach utilizes depth information as privileged data in the context of Unsupervised Domain Adaptation (UDA) for semantic segmentation. The research acknowledges the difficulty posed by the domain gap that occurs when models trained on data from one domain (like synthetic images) are applied to another domain (such as real-world images). DADA seeks to bridge this gap by leveraging depth information available in the source domain, enabling improved performance on the target domain despite the absence of labeled target data.

The framework encompasses a depth-aware learning strategy that integrates depth into several facets of the adaptation process. A novel depth-aware architecture is proposed, incorporating a depth regression task into the segmentation network. The architecture combines depth-specific features with standard CNN appearance features through residual fusion, facilitating better semantic predictions on the target domain.

Key Contributions

1. Depth-aware UDA Learning Strategy: The proposed framework aligns both segmentation-based and depth-based claims across source and target domains while being cognizant of scene geometry.
2. Depth-aware Architecture: The novel pipeline includes a depth prediction task that fuses its outputs with the standard CNN features before feeding them into segmentation classifiers, thus enriching visual representations with geometric information.
3. Performance Evaluation: The approach achieves state-of-the-art results on various synthetic-to-real benchmarks. Extensive experimental analysis reveals iterative performance improvements leveraged by the depth-aware modifications.

Experimental Validation

The proposed DADA was evaluated extensively using synthetic datasets such as SYNTHIA, coupled with real-world datasets like Cityscapes and Mapillary Vistas. The architecture exhibited significant improvements over other methods. For instance, on SYNTHIA to Cityscapes semantic segmentation tasks (16 classes), DADA demonstrated a Mean Intersection over Union (mIoU) of 42.6%, surpassing prior methods such as AdvEnt and SPIGAN. Detailed per-class improvements were observed, especially with vehicles and human categories, underscoring the benefit of integrating depth information.

Implications

The successful implementation of depth-aware UDA for semantic segmentation highlights the potential of incorporating geometric information into visual domain adaptation tasks. Practically, this can enhance model robustness in autonomous systems like self-driving cars, where diverse environmental conditions may otherwise impair performance. Theoretically, it opens avenues for further exploration in multi-modal domain adaptation and suggests that auxiliary tasks can significantly impact primary task outcomes even when source data is limited.

Future Perspectives

Future research directions could investigate the application of DADA in scenarios where depth information is sparse, such as via LiDAR sensors, forming a robust framework for enhancing real-world vehicle perception systems. Further tuning of the balance between main and auxiliary tasks might refine performance even more. Exploring the integration of other forms of privileged information and adapting this framework for extensible tasks beyond segmentation could yield additional advances in AI model adaptation.

In conclusion, this paper illustrates the efficacy of depth-aware strategies in overcoming domain gaps and contributes a methodologically sound approach that could broadly influence subsequent developments in adaptive intelligent systems.