AllSpark: Reborn Labeled Features for Semi-Supervised Semantic Segmentation in Transformers
提案されたAllSparkは、ラベル付きデータを再生するためにラベルなしの特徴を活用し、既存の方法を凌駕します。
Effiziente Semi-Supervised Semantic Segmentation mit AllSpark
AllSpark verbessert die Semi-Supervised Semantic Segmentation durch die Wiedergeburt von markierten Merkmalen aus unmarkierten Merkmalen.
Efficient Semi-Supervised Semantic Segmentation of Remote Sensing Imagery using Decision and Feature Diversification
The core message of this paper is to propose two efficient semi-supervised learning architectures, DiverseHead and DiverseModel, that leverage multi-head and multi-model approaches to enhance the precision and diversity of pseudo labels during training, leading to improved semantic segmentation performance on remote sensing imagery datasets.
Leveraging Dual-Level Contrastive Learning and Class-Aware Pseudo-Labeling for Improved Semi-Supervised Semantic Segmentation
The proposed Dual-Level Siamese Structure Network (DSSN) effectively leverages both labeled and unlabeled data to improve semi-supervised semantic segmentation performance, by utilizing dual-level contrastive learning and a novel class-aware pseudo-label generation strategy.
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