Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation (2204.02548v2)

Abstract: In this paper, we study the task of synthetic-to-real domain generalized semantic segmentation, which aims to learn a model that is robust to unseen real-world scenes using only synthetic data. The large domain shift between synthetic and real-world data, including the limited source environmental variations and the large distribution gap between synthetic and real-world data, significantly hinders the model performance on unseen real-world scenes. In this work, we propose the Style-HAllucinated Dual consistEncy learning (SHADE) framework to handle such domain shift. Specifically, SHADE is constructed based on two consistency constraints, Style Consistency (SC) and Retrospection Consistency (RC). SC enriches the source situations and encourages the model to learn consistent representation across style-diversified samples. RC leverages real-world knowledge to prevent the model from overfitting to synthetic data and thus largely keeps the representation consistent between the synthetic and real-world models. Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning. SHM selects basis styles from the source distribution, enabling the model to dynamically generate diverse and realistic samples during training. Experiments show that our SHADE yields significant improvement and outperforms state-of-the-art methods by 5.05% and 8.35% on the average mIoU of three real-world datasets on single- and multi-source settings, respectively.

• The paper introduces SHADE, a framework using Style-Hallucinated Dual Consistency Learning to address domain shift in synthetic-to-real semantic segmentation.
• SHADE employs Style Consistency (SC), Retrospection Consistency (RC), and a Style Hallucination Module (SHM) to improve generalization across varying data styles and bridge the synthetic-real gap.
• The method achieves strong numerical performance, significantly improving mean IoU scores over state-of-the-art baselines on benchmark datasets for domain generalized semantic segmentation.

An Analysis of Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation

The paper in question examines the complex task of synthetic-to-real domain generalized semantic segmentation. This task is significant in contexts such as autonomous driving, where the deployment of a model trained on readily available synthetic data to real-world environments is critically hindered by domain shifts. The proposed framework, Style-Hallucinated Dual Consistency Learning (SHADE), is presented as a response to these challenges, emphasizing robust performance on unseen real-world scenes by addressing the domain shift between synthetic and real-world data.

Core Methodology

SHADE introduces dual consistency constraints—Style Consistency (SC) and Retrospection Consistency (RC)—to confront the domain shift issue. These constraints are used to ensure that the model learns consistent representations regardless of variations in style (SC) while also leveraging implicit real-world knowledge (RC) to avoid overfitting to synthetic data. This is further supplemented by a Style Hallucination Module (SHM), which dynamically generates style-diversified samples for training.

1. Style Consistency (SC): This mechanism aims to stabilize the output of the model across samples of varying styles by utilizing logit pairing, compelling the model to focus on style-invariant features, which are crucial for generalization across domains.
2. Retrospection Consistency (RC): The approach uses the knowledge encoded in pre-trained ImageNet models to guide the model towards real-world feature distributions, thus bridging the synthetic-real gap at a feature level.
3. Style Hallucination Module (SHM): The SHM generates new training samples by selecting and combining diverse basis styles from the source data using a method inspired by farthest point sampling (FPS). This ensures a broad coverage of potential style variations without dependence on real-world data.

Strong Numerical Performance

The paper reports that their proposed framework significantly improves performance over baseline models and other state-of-the-art domain generalization methods across multiple datasets. Specifically, SHADE yields substantial improvement on the mean IoU scores, outperforming methods like IBN-Net, ISW, DRPC, and FSDR. The results are consistent across different settings, including single-source (e.g., only GTAV) and multi-source (e.g., GTAV + SYNTHIA) domain generalization tasks. This demonstrates SHADE's ability to enhance the robustness of semantic segmentation models in unpredictable real-world scenarios.

Impact and Future Directions

Practically, SHADE holds the potential to improve autonomous systems such as self-driving cars by reducing dependency on vast quantities of real-world training data, thus lowering costs and accelerating the deployment cycle of new models. Theoretically, this work pushes forward the boundaries of domain generalization, providing insights into how latent real-world features can be utilized without direct reliance on additional real-world annotations.

Looking ahead, one potential pathway is the refinement of SHM to explore new forms of style variation or to optimize the basis selection process further. Additionally, adapting SHADE to other domains or tasks beyond semantic segmentation could provide new insights into its versatility and underlying mechanisms. The integration of these techniques in end-to-end learning pipelines or their hybridization with other domain adaptation strategies could also yield fascinating results.

In conclusion, SHADE offers a nuanced approach to the synthetic-to-real semantic segmentation problem, providing a solid bedrock for further explorations aimed at transcending domain barriers in machine learning applications.