Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation (2003.08040v3)

Abstract: We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work. State-of-the-art approaches prove that performing semantic-level alignment is helpful in tackling the domain shift issue. Based on the observation that stuff categories usually share similar appearances across images of different domains while things (i.e. object instances) have much larger differences, we propose to improve the semantic-level alignment with different strategies for stuff regions and for things: 1) for the stuff categories, we generate feature representation for each class and conduct the alignment operation from the target domain to the source domain; 2) for the thing categories, we generate feature representation for each individual instance and encourage the instance in the target domain to align with the most similar one in the source domain. In this way, the individual differences within thing categories will also be considered to alleviate over-alignment. In addition to our proposed method, we further reveal the reason why the current adversarial loss is often unstable in minimizing the distribution discrepancy and show that our method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains. We conduct extensive experiments in two unsupervised domain adaptation tasks, i.e. GTA5 to Cityscapes and SYNTHIA to Cityscapes, and achieve the new state-of-the-art segmentation accuracy.

Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation

The paper "Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation" explores a refined strategy for addressing the domain shift problem often encountered in unsupervised domain adaptation (UDA) for semantic segmentation. Semantic segmentation is a pivotal process in computer vision, aiming for pixel-level scene understanding, which is a prerequisite for applications like autonomous driving. The primary challenge in UDA is the transition between a source domain, typically comprising synthetic data, to a target domain consisting of real-world data.

The authors propose an innovative method by considering the intrinsic differences between "stuff" (amorphous regions such as sky and road) and "things" (distinct object instances like cars and people). The paper introduces a novel approach to semantic-level alignment by applying distinct strategies to these two categories. Specifically:

1. Stuff Categories: The model generates feature representations for each class of stuff and aligns these representations from the target domain to the source domain. This alignment is facilitated by generating a global feature vector for each stuff category and pulling the target domain features towards the source domain features.
2. Thing Categories: For things, the model generates feature representations for individual instances. Each instance feature in the target domain is aligned with the most similar instance feature from the source domain, thus accounting for the inherent variation within thing categories and reducing over-alignment.

The paper identifies the limitations of current adversarial approaches within Generative Adversarial Networks (GANs), which primarily minimize distribution discrepancies globally and may result in instability during training. The authors demonstrate how their methodology counters this instability by leveraging differential treatment for matching specific features between domains.

The experimental validation is performed on two significant UDA tasks: GTA5 to Cityscapes, and SYNTHIA to Cityscapes. The proposed method attains superior state-of-the-art segmentation accuracy. Specifically, using a ResNet101 backbone, the method reports a segmentation accuracy presenting a notable improvement over existing techniques.

Implications and Future Directions

The proposed differential treatment approach offers several practical and theoretical implications for semantic segmentation and UDA:

• Precision in Feature Alignment: By tailoring strategies for "stuff" and "things," the approach subtly enhances the feature alignment whereby the model respects intra-category variations. This results in better preservation of semantic context.
• Stability in Adversarial Training: The method alleviates the instability problem commonly associated with adversarial losses in GAN training frameworks, delivering a more stable adaptation process across extensive training iterations.
• Extension to Multi-Domain Adaptation: While focused on the single-source to single-target domain shift, a logical extension would be adapting this method to scenarios with multiple source domains or target domains.

Future work may explore the adaptation of this methodology to real-time applications, where computational efficiency is paramount. Moreover, analysing similar differential treatments in other tasks, such as object detection and instance segmentation, could reveal further insights.

The paper successfully introduces a nuanced method for unsupervised domain adaptation, paving the way for improved semantic understanding across diverse environments, a critical requirement for deploying robust AI systems in the real world.