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Found 8 papers, 5 papers with code
Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention
To address these problems, this paper proposes a novel semi-supervised Multi-Scale Uncertainty and Cross-Teacher-Student Attention (MUCA) model for RS image semantic segmentation tasks.
Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction with Extremely Limited Labels
Due to the costliness of labelled data in real-world applications, semi-supervised learning, underpinned by pseudo labelling, is an appealing solution.
Semi-supervised Object Detection via Virtual Category Learning
Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing.
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation
Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation.
High-Order Paired-ASPP Networks for Semantic Segmenation
The network first introduces a High-Order Representation module to extract the contextual high-order information from all stages of the backbone.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Highlight Every Step: Knowledge Distillation via Collaborative Teaching
Knowledge distillation aims to train a compact student network by transferring knowledge from a larger pre-trained teacher model.
Few-shot Learning for Domain-specific Fine-grained Image Classification
Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications.
