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Found 4 papers, 3 papers with code
Semi-Supervised Domain Adaptation for Wildfire Detection
In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries.
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong augmented input pair without losing label information.
Structured Consistency Loss for semi-supervised semantic segmentation
We propose a structured consistency loss to address this limitation of extant studies.
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