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Found 6 papers, 2 papers with code
Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data
Airborne LiDAR systems have the capability to capture the Earth's surface by generating extensive point cloud data comprised of points mainly defined by 3D coordinates.
Time-based Self-supervised Learning for Wireless Capsule Endoscopy
State-of-the-art machine learning models, and especially deep learning ones, are significantly data-hungry; they require vast amounts of manually labeled samples to function correctly.
WCE Polyp Detection with Triplet based Embeddings
Automatic image analysis methods can be used to reduce the time needed for physicians to evaluate a capsule endoscopy video, however these methods are still in a research phase.
Uncertainty Gated Network for Land Cover Segmentation
The production of thematic maps depicting land cover is one of the most common applications of remote sensing.
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
Most of the CAD systems in the capsule endoscopy share a common system design, but use very different image and video representations.
Learning to count with deep object features
Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective.
