Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation (2207.06654v1)

Abstract: Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easily discriminated as done on the source domain anymore. Instead, ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation. By considering the same class prototypes as positives and other class prototypes as negatives to achieve class-centered distribution alignment, ProCA achieves state-of-the-art performance on classical domain adaptation tasks, {\em i.e., GTA5 $\to$ Cityscapes \text{and} SYNTHIA $\to$ Cityscapes}. Code is available at \href{https://github.com/jiangzhengkai/ProCA}{ProCA}

• The paper introduces Prototypical Contrast Adaptation (ProCA), a novel unsupervised domain adaptation strategy for semantic segmentation leveraging prototypical contrastive learning to align features across domains.
• ProCA employs a multi-level adaptation approach at both feature and output levels, using class-wise prototypes that are dynamically updated to ensure discriminative and domain-invariant representations.
• Experiments show ProCA achieves state-of-the-art semantic segmentation results on standard benchmarks, notably reaching 56.3% mIoU on GTA5 to Cityscapes and 53.0% mIoU on SYNTHIA to Cityscapes.

Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation

The paper "Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation" introduces a novel strategy, termed Prototypical Contrast Adaptation (ProCA), to advance the capabilities of Unsupervised Domain Adaptation (UDA) in semantic segmentation. The authors recognize the intrinsic difficulties posed by domain shifts, where models trained on a labeled source domain often stumble when applied to an unlabeled target domain. Their approach aims to address this by utilizing prototypical contrastive learning to facilitate effective feature alignment across domains.

Core Contributions

1. Prototypical Contrastive Framework: ProCA introduces class-wise prototypes as central elements in adapting feature representations between source and target domains. Unlike prior methods that largely focus on intra-class alignment, ProCA explicitly incorporates inter-class information, leveraging prototypes as both positive and negative samples in a contrastive learning setting. This strategy promotes a more nuanced alignment that preserves the discrimination between different classes once representations are adapted to the target domain.
2. Multi-level Adaptation Approach: The adaptation process in ProCA operates on both feature-level and output-level. Prototypes initialized at the feature level are periodically updated using both source and target domain data, ensuring that these representations remain both domain-invariant and discriminative. At the output level, the expansion to class-wise distribution alignment further strengthens the model’s ability to generalize across domain boundaries.
3. Enhanced Self-training with Adaptive Thresholds: The authors augment ProCA with a self-training method using class-specific adaptive thresholds for pseudo-label generation. This balances the distribution of pseudo-label confidence across classes and integrates them into the training cycle, enabling iterative refinement and robust target domain adaptation.

Experimental Results

The effectiveness of ProCA is demonstrated through rigorous experiments on two prominent benchmarks: GTA5 $\to$ Cityscapes and SYNTHIA $\to$ Cityscapes. Using a DeepLab-v2 network with a ResNet-101 backbone, the method achieves state-of-the-art results, with corresponding mean Intersection over Union (mIoU) rates of 56.3\% and 53.0\% for the respective datasets. These figures mark significant improvements over existing approaches, particularly in handling challenging classes such as "train," where ProCA outperforms both adversarial and other prototype-based methods like ProDA.

Implications and Future Directions

The introduction of ProCA highlights the importance of inter-class relationships in semantic segmentation domain adaptation. The integration of prototypes offers a robust mechanism for ensuring representational consistency between domains, facilitating improved segmentation performance in practical applications. On the theoretical front, the method suggests possible extensions into other domain adaptation tasks in computer vision, such as object detection and instance segmentation, where class discrimination is similarly pivotal.

Future research could explore the application of prototypical contrast adaptation in multi-domain or continuous domain adaptation scenarios, as well as its integration into transformer-based architectures. The dynamic nature of prototype updating presents a potential avenue for applications that require real-time data processing across evolving domains. Further investigations into prototype initialization and updating strategies could yield insights into optimal adaptation processes across diverse data types.