A survey: researches advanced in weakly supervised fine-grained visual classification

Abstract

With the development of multimedia technology, Fine-Grained Visual Classification (FGVC) has gradually become one of the new hot tasks in computer vision community, whose goal is to identify images that belong to the same species. Though the accuracy of FGVC has made a great breakthrough, the performance is still limited by the issue of locating objects' discriminative regions, as common state-of-the-art convolutional neural networks that perform excellently in image classification task such as ImageNet-1k cannot be directly applied to FGVC tasks. In this case, we provide a comprehensive and systematic survey of recent advances in FGVC field and divide the existing methods into: creative application of attentive structures, aids of diverse pretraining methods, various designs of loss functions and other innovative methods. We further analyze the performance of representative methods on common data sets, and finally summarize the existing problems in the FGVC research field and predict the solutions to these problems in the future.