  WebFG 2020 is an international challenge hosted by Nanjing University of
Science and Technology, University of Edinburgh, Nanjing University, The
University of Adelaide, Waseda University, etc. This challenge mainly pays
attention to the webly-supervised fine-grained recognition problem. In the
literature, existing deep learning methods highly rely on large-scale and
high-quality labeled training data, which poses a limitation to their
practicability and scalability in real world applications. In particular, for
fine-grained recognition, a visual task that requires professional knowledge
for labeling, the cost of acquiring labeled training data is quite high. It
causes extreme difficulties to obtain a large amount of high-quality training
data. Therefore, utilizing free web data to train fine-grained recognition
models has attracted increasing attentions from researchers in the fine-grained
community. This challenge expects participants to develop webly-supervised
fine-grained recognition methods, which leverages web images in training
fine-grained recognition models to ease the extreme dependence of deep learning
methods on large-scale manually labeled datasets and to enhance their
practicability and scalability. In this technical report, we have pulled
together the top WebFG 2020 solutions of total 54 competing teams, and discuss
what methods worked best across the set of winning teams, and what surprisingly
did not help.
