Unsupervised learning based object detection using Contrastive Learning

arXiv:2402.13465v1 Announce Type: new
Abstract: Training image-based object detectors presents formidable challenges, as it entails not only the complexities of object detection but also the added intricacies of precisely localizing objects within potentially diverse and noisy environments. However, the collection of imagery itself can often be straightforward; for instance, cameras mounted in vehicles can effortlessly capture vast amounts of data in various real-world scenarios. In light of this, we introduce a groundbreaking method for training single-stage object detectors through unsupervised/self-supervised …

abstract arxiv cameras challenges collection complexities cs.cv detection diverse environments image instance objects training type unsupervised unsupervised learning