Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank (2104.13415v3)

Abstract: This work presents a novel approach for semi-supervised semantic segmentation. The key element of this approach is our contrastive learning module that enforces the segmentation network to yield similar pixel-level feature representations for same-class samples across the whole dataset. To achieve this, we maintain a memory bank continuously updated with relevant and high-quality feature vectors from labeled data. In an end-to-end training, the features from both labeled and unlabeled data are optimized to be similar to same-class samples from the memory bank. Our approach outperforms the current state-of-the-art for semi-supervised semantic segmentation and semi-supervised domain adaptation on well-known public benchmarks, with larger improvements on the most challenging scenarios, i.e., less available labeled data. https://github.com/Shathe/SemiSeg-Contrastive

• The paper introduces a novel semi-supervised framework that leverages a teacher-student model with pixel-level contrastive learning.
• It utilizes a class-wise memory bank to align features from both labeled and unlabeled data, significantly enhancing segmentation accuracy.
• Experimental results on Cityscapes and Pascal VOC benchmarks demonstrate robust improvements, especially with limited labeled data.

Semi-Supervised Semantic Segmentation via Pixel-Level Contrastive Learning

This essay provides an in-depth analysis of a paper that introduces a novel methodology for semi-supervised semantic segmentation, emphasizing the use of pixel-level contrastive learning facilitated by a class-wise memory bank. The research elevates the capacity of segmentation networks to learn more effectively from limited labeled data by leveraging a strategic contrastive learning framework.

Semantic segmentation plays a critical role in various computer vision applications by assigning semantic labels to individual pixels within an image. The challenge lies in the extensive need for labeled datasets, which require labor-intensive per-pixel annotations. This paper tackles these challenges within a semi-supervised framework, harnessing the power of unlabeled data.

Fundamental Approach

The approach is structured around a teacher-student model. The student network is enhanced through a blend of supervised and unsupervised learning techniques. At the core is the use of a memory bank, populated with high-quality, pixel-level features extracted by the teacher network from labeled data. This memory bank orchestrates a contrastive learning process whereby both labeled and unlabeled data are aligned in the feature space.

The paper posits that pixel-level contrastive learning facilitates robust class separation in feature space, significantly aiding semantic segmentation performance, especially in low-label scenarios. The contrastive module is powered by positive-only similarity maximization across class-specific features from the memory bank and student network output.

Results and Implications

The method's efficacy is validated across several benchmarks, including Cityscapes and Pascal VOC, where it consistently outperforms existing state-of-the-art methods. Remarkably, the benefits are accentuated in scenarios where labeled data is scarce, underscoring the approach's robustness. This methodology also addresses semi-supervised domain adaptation tasks effectively, aligning cross-domain features with those from the target domain via the memory bank mechanism.

Technical Insights and Future Directions

The numerical results demonstrate the power of leveraging high-quality source features to refine the output of a student segmentation model. The paper documents increasing improvement margins corresponding to reduced availability of labeled data, signaling a promising direction for semi-supervised tasks. This opens avenues to potentially expand this framework beyond semantic segmentation to other vision-based tasks such as object detection.

From a technical perspective, the per-class memory bank inherently aligns student outputs with robust, class-specific learned representations, offering insights into optimal class separation and feature alignment in semi-supervised learning environments. Future work will likely explore adaptable memory bank configurations and extension into tasks beyond pixel-level labeling, as well as optimizing the architecture for computational efficiency.

In conclusion, this paper provides a substantial contribution by reframing semantic segmentation within a semi-supervised context, embedding a sophisticated contrastive learning framework that significantly advances performance metrics on established benchmarks. The work affirms the potential for contrastive learning to offer robust solutions in domains characterized by limited labeled data forces, prompting further exploration of similar methodologies across a broader problem space in artificial intelligence.