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Semi-supervised Learning for Dense Object Detection in Retail Scenes
[article]
2021
arXiv
pre-print
Retail scenes usually contain densely packed high number of objects in each image. Standard object detection techniques use fully supervised training methodology. This is highly costly as annotating a large dense retail object detection dataset involves an order of magnitude more effort compared to standard datasets. Hence, we propose semi-supervised learning to effectively use the large amount of unlabeled data available in the retail domain. We adapt a popular self supervised method called
arXiv:2107.02114v1
fatcat:yqodx3cpk5brzabttpmm64cugy