March 13, 2024, 4:42 a.m. | Nieves Crasto

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07113v1 Announce Type: cross
Abstract: Object detection, a pivotal task in computer vision, is frequently hindered by dataset imbalances, particularly the under-explored issue of foreground-foreground class imbalance. This lack of attention to foreground-foreground class imbalance becomes even more pronounced in the context of single-stage detectors. This study introduces a benchmarking framework utilizing the YOLOv5 single-stage detector to address the problem of foreground-foreground class imbalance. We crafted a novel 10-class long-tailed dataset from the COCO dataset, termed COCO-ZIPF, tailored to reflect …

arxiv class cs.cv cs.lg detection diagnosis experimental object strategies study type

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