WildNet: Learning Domain Generalized Semantic Segmentation from the Wild (2204.01446v1)

Abstract: We present a new domain generalized semantic segmentation network named WildNet, which learns domain-generalized features by leveraging a variety of contents and styles from the wild. In domain generalization, the low generalization ability for unseen target domains is clearly due to overfitting to the source domain. To address this problem, previous works have focused on generalizing the domain by removing or diversifying the styles of the source domain. These alleviated overfitting to the source-style but overlooked overfitting to the source-content. In this paper, we propose to diversify both the content and style of the source domain with the help of the wild. Our main idea is for networks to naturally learn domain-generalized semantic information from the wild. To this end, we diversify styles by augmenting source features to resemble wild styles and enable networks to adapt to a variety of styles. Furthermore, we encourage networks to learn class-discriminant features by providing semantic variations borrowed from the wild to source contents in the feature space. Finally, we regularize networks to capture consistent semantic information even when both the content and style of the source domain are extended to the wild. Extensive experiments on five different datasets validate the effectiveness of our WildNet, and we significantly outperform state-of-the-art methods. The source code and model are available online: https://github.com/suhyeonlee/WildNet.

• The paper proposes WildNet, a novel framework for domain generalized semantic segmentation that leverages unlabeled wild images to improve performance on unseen domains.
• WildNet employs four key techniques including feature stylization, content extension, style extension, and semantic consistency regularization to achieve generalization.
• Experimental results show WildNet significantly outperforms state-of-the-art methods on various unseen domain datasets, confirming its superior generalization capability.

Domain Generalized Semantic Segmentation Using WildNet

In the paper "WildNet: Learning Domain Generalized Semantic Segmentation from the Wild," the authors present a novel approach to tackle domain generalization issues in semantic segmentation tasks. Semantic segmentation traditionally suffers while dealing with unseen domains due to overfitting to the styles and contents of the source domain, and this paper proposes an innovative solution through the WildNet framework. A key strategy involves leveraging unlabeled wild images to learn domain-generalized features without the need for specific target domain information during training.

Core Concepts and Methodology

Key Challenges in Domain Generalization:

Domain generalization involves creating models that perform well on unseen domains, usually suffering due to overfitting to the features of the source domains. Traditionally, approaches focusing only on style diversification miss enhancing the model's ability to generalize across a wider spectrum of unseen content.

Introduction of WildNet Framework:

The authors propose WildNet, which uniquely extends both content and style to the wild domain by utilizing unlabeled datasets like ImageNet. The approach is centered around four main techniques:

1. Feature Stylization (FS): Utilizes adaptive instance normalization layers to diversify styles by swapping statistical properties between source and wild features.
2. Content Extension Learning (CEL): Uses contrastive learning adapted for pixel-wise semantic content, promoting intra-class variability and preventing content-specific overfitting.
3. Style Extension Learning (SEL): Encourages networks to learn task-specific information from the wild-stylized source features, making the model more robust to style variations.
4. Semantic Consistency Regularization (SCR): Regularizes the network to ensure consistent semantic predictions despite variations in features' style and content.

These methodologies are combined in a unified model to achieve superior generalization performance on unseen domain datasets.

Numerical Results and Comparative Analysis

The efficacy of WildNet was quantitatively validated across multiple unseen and seen domains. It was shown to outperform previous state-of-the-art methods such as RobustNet and IBN-Net significantly across various benchmarks. Specifically, the paper demonstrates superior mean IoU scores across unseen datasets, utilizing backbones including ResNet-50, ResNet-101, and VGG-16, emphasizing the robustness and versatility of the proposed method.

Implications and Future Directions

Theoretical Implications:

WildNet importantly expands prior understandings by focusing on both style and content generalization. The approach suggests future studies could explore less restrictive content diversification strategies and their impact on generalization capabilities.

Practical Implementations:

Utilizing unlabeled data like ImageNet efficiently offers practical implications in reducing dependency on labeled data in specific domains. This could lead to more adaptable AI systems in dynamic and varied real-world environments.

Future Research Directions:

Future investigations might explore the accommodation for wild-only class extensions, addressing current limitations and enhancing the method's capability to generalize across completely unseen scenarios. Also, further research might refine content selection criteria for class discrimination.

Conclusion

This paper marks a significant advancement in domain generalized semantic segmentation. By accounting for both styles and contents in network training, WildNet presents a flexible yet powerful framework to enhance the generalization capacity for semantic segmentation models. This advancement opens new avenues for utilizing vast amounts of unlabeled data in addressing the challenges of unseen domain predictions in varied application contexts.