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Found 4 papers, 1 papers with code
Region-level Active Detector Learning
Active learning for object detection is conventionally achieved by applying techniques developed for classification in a way that aggregates individual detections into image-level selection criteria.
Minimax Active Learning
Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator.
Overhead Detection: Beyond 8-bits and RGB
Specifically, we examine how various features of the data affect building detection accuracy with respect to the Intersection over Union metric.
xView: Objects in Context in Overhead Imagery
We introduce a new large-scale dataset for the advancement of object detection techniques and overhead object detection research.
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