Papers Few-Shot Classification and Segmentation
“Few-Shot Classification and Segmentation” 태그가 달린 논문 4편 · 필터 해제
Text Augmented Correlation Transformer For Few-shot Classification & Segmentation
Foundation models like CLIP and ALIGN have transformed few-shot and zero-shot vision applications by fusing visual and textual data, yet the integrative few-shot classification and segmentation (FS-CS) task primarily…
Few-Shot Classification and SegmentationFew-Shot LearningSegmentationSAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs
Due to its all-weather and day-and-night capabilities, Synthetic Aperture Radar imagery is essential for various applications such as disaster management, earth monitoring, change detection and target recognition. Howeve…
Change DetectionContrastive LearningData AugmentationFew-Shot Classification and Segmentation+2Class-Specific Channel Attention for Few-Shot Learning
Few-Shot Learning (FSL) has attracted growing attention in computer vision due to its capability in model training without the need for excessive data. FSL is challenging because the training and testing categories (the …
Few-Shot Classification and SegmentationFew-Shot Image ClassificationFew-Shot LearningImage Classification+2Integrative Few-Shot Learning for Classification and Segmentation
We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples. This tas…
ClassificationFew-Shot Classification and SegmentationFew-Shot LearningFew-Shot Semantic Segmentation+3