Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach (1908.09547v1)

Abstract: We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic source domains to real target domains. Our approach draws on an insight connecting two existing works: curriculum domain adaptation and self-training. Inspired by the former, PyCDA constructs a pyramid curriculum which contains various properties about the target domain. Those properties are mainly about the desired label distributions over the target domain images, image regions, and pixels. By enforcing the segmentation neural network to observe those properties, we can improve the network's generalization capability to the target domain. Motivated by the self-training, we infer this pyramid of properties by resorting to the semantic segmentation network itself. Unlike prior work, we do not need to maintain any additional models (e.g., logistic regression or discriminator networks) or to solve minmax problems which are often difficult to optimize. We report state-of-the-art results for the adaptation from both GTAV and SYNTHIA to Cityscapes, two popular settings in unsupervised domain adaptation for semantic segmentation.

• The paper introduces the PyCDA framework, which integrates pyramid curriculums with self-training to bridge the synthetic-real domain gap.
• It employs multi-scale pixel squares to capture both local and global image features, achieving improved mIoU on datasets like GTAV to Cityscapes.
• The non-adversarial method simplifies optimization and reduces computational complexity, paving the way for practical real-world deployments.

Analysis of "Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach"

The paper addresses the challenging problem of cross-domain semantic segmentation, specifically focusing on transforming models trained on synthetic datasets to perform effectively on real-world data. This task is notably significant in scenarios like urban scene understanding where data collection is cumbersome, and leveraging synthetic data generated from simulators like GTAV and SYNTHIA is both efficient and economical.

Key Approach

The core contribution lies in the self-motivated pyramid curriculum domain adaptation (PyCDA) framework, a non-adversarial method designed to bridge the domain gap effectively. Unlike adversarial methods that often necessitate complex training regimes and auxiliary models, PyCDA seeks simplicity and directness. The innovation forms from a conceptual alignment of curriculum domain adaptation (CDA) techniques with self-training (ST) methods.

Through PyCDA, the authors leverage a pyramid structure comprising various abstraction levels—from pixel-wise annotations to region-specific labels and full image distributions. This hierarchy facilitates a comprehensive learning process where the segmentation model incrementally understands and adapts to domain disparities at different granularities.

Methodological Insights

1. Pyramid Structure: PyCDA integrates properties from both global and local image features. By using multi-scale pixel squares as layers within the pyramid, it improves upon existing methods that relied solely on superpixels. This adjustment has shown to be computationally less expensive and more effective.
2. Self-Motivated Inference: The methodology includes leveraging the segmentation model itself to infer pseudo-labels and distribution properties, removing the reliance on external models like logistic regressions or discriminators typically used in CDA.
3. Non-adversarial Strategy: The approach reduces complexity by avoiding adversarial objectives, leading to easier optimization scenarios without the instability associated with GAN-based models.

Results and Evaluation

The performance of PyCDA is evaluated across benchmarks involving transitions from GTAV and SYNTHIA to Cityscapes, with significant improvements realized in mean Intersection-over-Union (mIoU) across various settings. Noteworthy is its ability to outperform state-of-the-art adversarial adaptation frameworks in multiple experiments while maintaining a simpler implementation, poised as a favorable choice for real-world deployments.

Implications and Future Work

The research underscores the viability of non-adversarial domain adaptation in semantic segmentation, particularly highlighting scenarios where computational simplicity and efficiency are pivotal. The paradigm of blending curriculum learning with self-training could foreseeably extend to other computer vision tasks, where exploiting inherent characteristics of datasets across domains could simplify domain adaptation processes.

Future exploration might expand into integrating more sophisticated hierarchical learning frameworks within PyCDA, improving label inferences at all hierarchical levels, and experimenting with alternative domain shift scenarios beyond urban segmentation. Additionally, further analysis on balancing inter-layer dependencies within the pyramid could enhance adaptability and performance.

Overall, the PyCDA presents a model adaptation framework that is conceptually simple yet robust in its application, paving the path for future advancements in cross-domain learning with an emphasis on semantic segmentation.