Pin the Memory: Learning to Generalize Semantic Segmentation (2204.03609v2)

Abstract: The rise of deep neural networks has led to several breakthroughs for semantic segmentation. In spite of this, a model trained on source domain often fails to work properly in new challenging domains, that is directly concerned with the generalization capability of the model. In this paper, we present a novel memory-guided domain generalization method for semantic segmentation based on meta-learning framework. Especially, our method abstracts the conceptual knowledge of semantic classes into categorical memory which is constant beyond the domains. Upon the meta-learning concept, we repeatedly train memory-guided networks and simulate virtual test to 1) learn how to memorize a domain-agnostic and distinct information of classes and 2) offer an externally settled memory as a class-guidance to reduce the ambiguity of representation in the test data of arbitrary unseen domain. To this end, we also propose memory divergence and feature cohesion losses, which encourage to learn memory reading and update processes for category-aware domain generalization. Extensive experiments for semantic segmentation demonstrate the superior generalization capability of our method over state-of-the-art works on various benchmarks.

• The paper introduces a memory-guided meta-learning framework that leverages categorical memory to extract domain-invariant semantic features.
• It employs memory divergence and feature cohesion losses to ensure discriminative and coherent feature representations across domains.
• Extensive experiments across benchmarks demonstrate significant generalization improvements without requiring target domain data during training.

Overview of "Pin the Memory: Learning to Generalize Semantic Segmentation"

The paper, titled "Pin the Memory: Learning to Generalize Semantic Segmentation," focuses on addressing the challenges of semantic segmentation in unseen domains. While the rise of deep neural networks has led to significant advancements in semantic segmentation, models often struggle to generalize well from a source domain to a new, unseen target domain. The authors of this paper propose a novel approach that leverages a memory-guided meta-learning framework to improve domain generalization capabilities.

Methodology

This research introduces a memory-guided domain generalization method, which is grounded in a meta-learning framework designed specifically for semantic segmentation. The key components of this approach include:

1. Categorical Memory: The method uses a memory module to abstract the conceptual knowledge of semantic classes into categorical memory slots. This memory acts as a repository of domain-agnostic class information which can be effectively reused across different domains.
2. Meta-Learning Framework: The model is trained using a meta-learning algorithm that simulates domain shifts. The training process is iterative, involving meta-training and meta-testing phases. During meta-training, the model learns to memorize domain-agnostic and distinct class information. During meta-testing, the model is tested on simulated unseen domains to validate its generalization capabilities.
3. Loss Functions: The approach introduces memory divergence and feature cohesion loss functions. The memory divergence loss is designed to enhance the discriminative power of memory slots by increasing the distance between different class representations. The feature cohesion loss ensures that features from the encoder remain coherent and aligned with the corresponding memory guidance.

Experimental Results

The authors conduct extensive experiments across multiple well-known benchmarks for semantic segmentation, such as Cityscapes, BDD100K, and Mapillary datasets. The proposed method demonstrates consistent improvements in generalization performance over state-of-the-art domain generalization methods. Notably, the performance gains are observed without requiring access to the target domain data during training. The model also competes favorably with some unsupervised domain adaptation methods.

Implications and Future Directions

The introduction of categorical memory within a meta-learning framework is a promising step towards more robust domain generalization in semantic segmentation. By focusing on domain-invariant class features, the approach attempts to bridge conceptual knowledge gaps between source and target domains.

The implications of this work are profound for real-world applications where models are deployed in dynamic environments, such as autonomous driving and medical image analysis. This method potentially reduces the need for costly and time-consuming data collection and labeling from all possible target environments.

Future work could explore extending this approach to more generalized segmentation tasks, including those involving open-set or non-closed classes. Additionally, further improvements in memory efficiency and computational complexity may enhance the scalability and deployment of such models.

In summary, this paper presents a well-structured and innovative approach to tackling the longstanding challenge of domain generalization in semantic segmentation, offering significant contributions to both theoretical understanding and practical application.