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Found 9 papers, 0 papers with code
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
To improve the performance of test-time domain adaptation, we propose a multi task consistency guided source-free test-time domain adaptation medical image segmentation method which ensures the consistency of the local boundary predictions and the global prototype representation.
Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation
This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net), including an evidential classifier and a vanilla classifier.
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs.
Position-Aware Relation Learning for RGB-Thermal Salient Object Detection
FRDF utilizes the directional information between object pixels to effectively enhance the intra-class compactness of salient regions.
Don't Stop Learning: Towards Continual Learning for the CLIP Model
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community.
Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation
In specific, mutual-prototype alignment enhances the information interaction between labeled and unlabeled data.
PixelGame: Infrared small target segmentation as a Nash equilibrium
FNs-player and FPs-player are designed with different strategies: One is to minimize FNs and the other is to minimize FPs.
Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction
Further, we propose a context encoding module to utilize the global predictor from the error map to enhance the feature representation and regularize the networks.
Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity
To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study.
