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Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition
[article]
2021
arXiv
pre-print
Fine-grained Visual Classification (FGVC) aims to identify objects from subcategories. It is a very challenging task because of the subtle inter-class differences. Existing research applies large-scale convolutional neural networks or visual transformers as the feature extractor, which is extremely computationally expensive. In fact, real-world scenarios of fine-grained recognition often require a more lightweight mobile network that can be utilized offline. However, the fundamental mobile
arXiv:2112.04223v1
fatcat:sn2wcwqsgbgv7l7puhgqpapcum