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Found 6 papers, 4 papers with code
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
As for the data, we show that the autonomous driving benchmarks are monotonous in nature, that is, they are not diverse in scenarios and dense in pedestrians.
Bridging the gap between paired and unpaired medical image translation
Medical image translation has the potential to reduce the imaging workload, by removing the need to capture some sequences, and to reduce the annotation burden for developing machine learning methods.
Generalizable Pedestrian Detection: The Elephant In The Room
Furthermore, we illustrate that diverse and dense datasets, collected by crawling the web, serve to be an efficient source of pre-training for pedestrian detection.
Ranked #3 on
Pedestrian Detection
on CityPersons
(using extra training data)
Mask-RCNN and U-net Ensembled for Nuclei Segmentation
Nuclei segmentation is both an important and in some ways ideal task for modern computer vision methods, e. g. convolutional neural networks.
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection
In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs.
Cell Tracking via Proposal Generation and Selection
Microscopy imaging plays a vital role in understanding many biological processes in development and disease.
