Web: http://arxiv.org/abs/2209.10471

Sept. 22, 2022, 1:12 a.m. | Sangyun Shin, Stuart Golodetz, Madhu Vankadari, Kaichen Zhou, Andrew Markham, Niki Trigoni

cs.LG updates on arXiv.org arxiv.org

Deep learning has led to great progress in the detection of mobile (i.e.
movement-capable) objects in urban driving scenes in recent years. Supervised
approaches typically require the annotation of large training sets; there has
thus been great interest in leveraging weakly, semi- or self-supervised methods
to avoid this, with much success. Whilst weakly and semi-supervised methods
require some annotation, self-supervised methods have used cues such as motion
to relieve the need for annotation altogether. However, a complete absence of
annotation …

arxiv detection driving lidar mobile

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