Revisiting Weakly Supervised Pre-Training of Visual Perception Models. (arXiv:2201.08371v1 [cs.CV])

Model pre-training is a cornerstone of modern visual recognition systems.
Although fully supervised pre-training on datasets like ImageNet is still the
de-facto standard, recent studies suggest that large-scale weakly supervised
pre-training can outperform fully supervised approaches. This paper revisits
weakly-supervised pre-training of models using hashtag supervision with modern
versions of residual networks and the largest-ever dataset of images and
corresponding hashtags. We study the performance of the resulting models in
various transfer-learning settings including zero-shot transfer. We also
compare our …

arxiv cv perception training