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näkemys - Unsupervised Semantic Segmentation
Unsupervised Semantic Segmentation of High-Density Multispectral Urban Airborne Laser Scanning Data Using a Deep Clustering Method
This research proposes GroupSP, a novel unsupervised deep learning approach for semantic segmentation of high-density multispectral airborne laser scanning (ALS) data, aiming to reduce manual annotation efforts while achieving comparable accuracy to supervised methods.
Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
Advances in unsupervised learning have led to significant progress in semantic segmentation by incorporating depth information to improve feature correlation and sampling techniques.
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
EAGLE introduces a novel approach, emphasizing object-centric representation learning for unsupervised semantic segmentation, addressing the challenge of inadequate segmentation of complex objects with diverse structures.
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