A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition
[article]
2020
arXiv
pre-print
Part-based approaches for fine-grained recognition do not show the expected performance gain over global methods, although being able to explicitly focus on small details that are relevant for distinguishing highly similar classes. We assume that part-based methods suffer from a missing representation of local features, which is invariant to the order of parts and can handle a varying number of visible parts appropriately. The order of parts is artificial and often only given by ground-truth
arXiv:2007.02080v1
fatcat:f6n6rthtizcpphcldc6dlp7puq