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Found 4 papers, 2 papers with code
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales.
Chart-RCNN: Efficient Line Chart Data Extraction from Camera Images
We collected two datasets consisting of real camera photos for evaluation.
Refine and Represent: Region-to-Object Representation Learning
Recent works in self-supervised learning have demonstrated strong performance on scene-level dense prediction tasks by pretraining with object-centric or region-based correspondence objectives.
Interpreting Audiograms with Multi-stage Neural Networks
Audiograms are a particular type of line charts representing individuals' hearing level at various frequencies.
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