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Found 6 papers, 3 papers with code
What to Hide from Your Students: Attention-Guided Masked Image Modeling
Transformers and masked language modeling are quickly being adopted and explored in computer vision as vision transformers and masked image modeling (MIM).
MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data
Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions.
Multi-Label Classification
Weakly supervised segmentation
+1
It Takes Two to Tango: Mixup for Deep Metric Learning
In this work, we aim to bridge this gap and improve representations using mixup, which is a powerful data augmentation approach interpolating two or more examples and corresponding target labels at a time.
Ranked #4 on
Metric Learning
on In-Shop
(using extra training data)
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing
Although deep neural networks hold the state-of-the-art in several remote sensing tasks, their black-box operation hinders the understanding of their decisions, concealing any bias and other shortcomings in datasets and model performance.
Explainable artificial intelligence
Multi-Label Classification
Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
\begin{abstract} The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics.
Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning
The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research.
