DepMatch: Boosting Semi-Supervised Semantic Segmentation by Exploring Depth Difference Knowledge.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Semi-supervised semantic segmentation (SSS) methods struggle with inter-class depth similarity and intra-class depth discrepancy in unlabeled data.
• Existing SSS approaches do not fully leverage the potential of depth information available in unlabeled datasets.

Purpose of the Study:

• To introduce DepMatch, a novel approach for SSS that utilizes depth difference knowledge to guide consistency learning.
• To enhance feature learning and spatial understanding in SSS models by effectively incorporating depth information.

Main Methods:

• Developed a Class-wise Depth Disparity Perception (CDDP) module to exploit depth difference information guided by class prediction priors.
• Constructed a depth-feature discrepancy set and selected reliable pixel pairs for inter-class depth disparity knowledge distillation.
• Applied exponential normalization for intra-category depth disparity and used entropy-based adaptive weighting for prioritizing high-entropy areas.
• Introduced the Uncertain Logit Disparity Regulation (ULDR) module to leverage depth variations at class boundaries for improved spatial understanding.

Main Results:

• DepMatch significantly improves performance when integrated as a plug-and-play module into popular SSS frameworks.
• The method demonstrates substantial performance gains across various visual encoders on five public benchmarks.
• The approach effectively addresses challenges of inter-class depth similarity and intra-class depth discrepancy.

Conclusions:

• DepMatch offers a simple yet effective solution for leveraging depth information in SSS.
• The proposed method enhances robustness and accuracy in semantic segmentation tasks using unlabeled depth data.
• DepMatch shows promise for advancing SSS research by improving feature learning and spatial awareness.