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

cs.LG updates on

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.lg detection diagnosis experimental object strategies study type

Senior Machine Learning Engineer

@ GPTZero | Toronto, Canada

ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)

@ HelloBetter | Remote

Senior Research Engineer/Specialist - Motor Mechanical Design

@ GKN Aerospace | Bristol, GB

Research Engineer (Motor Mechanical Design)

@ GKN Aerospace | Bristol, GB

Senior Research Engineer (Electromagnetic Design)

@ GKN Aerospace | Bristol, GB

Associate Research Engineer Clubs | Titleist

@ Acushnet Company | Carlsbad, CA, United States