Search Results for author:
Found 5 papers, 1 papers with code
Relational Representation Learning Network for Cross-Spectral Image Patch Matching
Therefore, an innovative relational representation learning idea is proposed for the first time, which simultaneously focuses on sufficiently mining the intrinsic features of individual image patches and the relations between image patch features.
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation
Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge of the source domain to the target one to improve the adaptability of the segmentation model in the target domain.
Learning Pseudo-Relations for Cross-domain Semantic Segmentation
Domain adaptive semantic segmentation aims to adapt a model trained on labeled source domain to the unlabeled target domain.
Unsupervised Outlier Detection using Memory and Contrastive Learning
Instead, we think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers.
AFD-Net: Aggregated Feature Difference Learning for Cross-Spectral Image Patch Matching
Image patch matching across different spectral domains is more challenging than in a single spectral domain.
