The article discusses the importance of few-shot learning techniques in remote sensing, emphasizing their potential to advance object detection and semantic segmentation tasks. Traditional approaches rely heavily on large annotated datasets, which are often challenging and costly to obtain. By leveraging few-shot learning, researchers aim to enhance the capability of deep learning models to generalize effectively from limited examples. The article reviews various advancements in this field, highlighting successful applications and ongoing challenges, particularly in processing aerial and satellite imagery efficiently.
Recent advancements in few-shot learning for object detection and segmentation in remote sensing enable more effective use of limited annotated training datasets, addressing a significant challenge.
In remote sensing, object detection and segmentation can localize and identify specific entities like vehicles and buildings while delineating land and water boundaries at pixel level.
Collection
[
|
...
]