Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (2106.14133v1)

Abstract: Semantic segmentation has made tremendous progress in recent years. However, satisfying performance highly depends on a large number of pixel-level annotations. Therefore, in this paper, we focus on the semi-supervised segmentation problem where only a small set of labeled data is provided with a much larger collection of totally unlabeled images. Nevertheless, due to the limited annotations, models may overly rely on the contexts available in the training data, which causes poor generalization to the scenes unseen before. A preferred high-level representation should capture the contextual information while not losing self-awareness. Therefore, we propose to maintain the context-aware consistency between features of the same identity but with different contexts, making the representations robust to the varying environments. Moreover, we present the Directional Contrastive Loss (DC Loss) to accomplish the consistency in a pixel-to-pixel manner, only requiring the feature with lower quality to be aligned towards its counterpart. In addition, to avoid the false-negative samples and filter the uncertain positive samples, we put forward two sampling strategies. Extensive experiments show that our simple yet effective method surpasses current state-of-the-art methods by a large margin and also generalizes well with extra image-level annotations.

• The paper introduces a novel directional context-aware consistency mechanism using DC Loss to enhance segmentation accuracy with limited labeled data.
• It implements two specialized sampling strategies to avoid false negatives and filter uncertain positives, ensuring robust feature alignment.
• Empirical results show significant improvements over state-of-the-art methods, promising broader applications in fine-grained image understanding.

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency

Semantic segmentation is a critical task in the field of computer vision, enabling the assignment of semantic labels to each pixel in an image. Recent advancements have significantly improved semantic segmentation technologies, predominantly relying on supervised learning methodologies that require large amounts of annotated training data. However, obtaining pixel-level annotations is labor-intensive and time-consuming, which limits the scalability of these approaches. The discussed paper addresses this limitation by focusing on the problem of semi-supervised semantic segmentation, where only a small fraction of the data is labeled while the majority remains unlabeled.

The authors propose a novel approach termed as "Directional Context-aware Consistency" for enhancing semi-supervised semantic segmentation. They introduce a mechanism for maintaining consistency in the contextual information between features of the same identity when presented in different contexts. The innovative aspect of their method lies in the use of Directional Contrastive Loss (DC Loss), which ensures that features of lower quality align towards their higher-quality counterparts, thus facilitating consistency in representation.

The proposed solution integrates two critical sampling strategies aimed at addressing common challenges in semi-supervised learning: 1) avoiding false-negative samples, and 2) filtering out uncertain positive samples. These strategies are employed to enhance the robustness of the representation learning framework, effectively mitigating the risk of models overfitting to limited labeled samples with over-reliance on contextual cues.

Empirical evaluations demonstrate the efficacy of the proposed method, with experimental results confirming that it substantially surpasses existing state-of-the-art approaches in semi-supervised semantic segmentation. Remarkably, the proposed framework also delivers significant improvements when extended to scenarios with additional image-level annotations.

From a theoretical perspective, the paper contributes to the ongoing discourse on achieving better model generalization under limited labeled data scenarios. Practically, the introduction of context-aware consistency provides a promising direction for developing more data-efficient learning technologies, potentially applicable across various domains requiring fine-grained image understanding. As research and development in AI and machine learning continue to evolve, methodologies such as this may play a critical role in reducing the reliance on densely labeled datasets, thereby democratizing access to robust semantic segmentation capabilities. Future work may extend further into refining these methods or exploring cross-modalities in vision and beyond, harnessing the potential of semi-supervised paradigms across diverse applications.