Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation (2007.09222v1)

Abstract: Despite great progress in supervised semantic segmentation,a large performance drop is usually observed when deploying the model in the wild. Domain adaptation methods tackle the issue by aligning the source domain and the target domain. However, most existing methods attempt to perform the alignment from a holistic view, ignoring the underlying class-level data structure in the target domain. To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains. We adopt a fine-grained domain discriminator that not only plays as a domain distinguisher, but also differentiates domains at class level. The traditional binary domain labels are also generalized to domain encodings as the supervision signal to guide the fine-grained feature alignment. An analysis with Class Center Distance (CCD) validates that our fine-grained adversarial strategy achieves better class-level alignment compared to other state-of-the-art methods. Our method is easy to implement and its effectiveness is evaluated on three classical domain adaptation tasks, i.e., GTA5 to Cityscapes, SYNTHIA to Cityscapes and Cityscapes to Cross-City. Large performance gains show that our method outperforms other global feature alignment based and class-wise alignment based counterparts. The code is publicly available at https://github.com/JDAI-CV/FADA.

Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation

The paper "Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation" addresses a key challenge in deploying semantic segmentation models across different domains, particularly when moving from a synthetic data training environment to real-world applications. The domain shift problem, characterized by a performance drop due to differing data distributions between source and target domains, is tackled through a fine-grained adversarial learning strategy. This approach strives for class-level feature alignment while preserving semantic structures across domains.

The authors introduce a fine-grained domain discriminator that not only differentiates samples by domain but also encodes class-level information. This novel discriminator operates at a more detailed level than traditional binary domain discriminators, enabling a more precise alignment of class-level features. Instead of relying on binary domain labels, the paper generalizes these labels to domain encodings, which incorporate class information, thereby guiding the alignment process more effectively.

The effectiveness of the proposed method is validated through experiments on three benchmark domain adaptation tasks: GTA5 to Cityscapes, SYNTHIA to Cityscapes, and Cityscapes to Cross-City. Across these tasks, the proposed method demonstrated notable improvements in performance metrics over existing global and class-wise feature alignment methods, underscoring the utility of fine-grained adversarial learning.

A detailed analysis using Class Center Distance (CCD) reveals that the method achieves better class-level alignment than the compared approaches. This is indicative of the fine-grained discriminator’s ability to maintain class-specific semantic structures during feature alignment. The paper reports significant improvements in mIoU scores, with results showing an increase of up to 16.4% for the SYNTHIA to Cityscapes task using different architectures like VGG-16 and ResNet-101.

The implications of this research are both practical and theoretical. Practically, it provides a viable solution for improving cross-domain semantic segmentation, which is vital for applications such as autonomous driving where models trained on synthetic data need to perform reliably in real-world conditions. Theoretically, the paper opens pathways for further exploration into adversarial learning strategies that incorporate more nuanced data structures beyond simple domain categorization.

Future developments could involve extending this approach to more complex scenarios with multiple domain shifts or combining it with other adaptation strategies to handle a broader range of tasks. The modularity of the method allows for adaptation to different architectures and could stimulate interest in utilizing fine-grained class structures in other machine learning domains beyond semantic segmentation.

Overall, "Classes Matter" contributes a sophisticated perspective on domain adaptation, emphasizing the importance of class-specific information in achieving robust and transferable models. The results indicate that addressing the inter-relationships of class-level structures during domain adaptation can significantly enhance the performance of semantic segmentation models in varied and challenging application environments.