Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published March 27, 2019 | Submitted
Report Open

The Devil is in the Tails: Fine-grained Classification in the Wild

Abstract

The world is long-tailed. What does this mean for computer vision and visual recognition? The main two implications are (1) the number of categories we need to consider in applications can be very large, and (2) the number of training examples for most categories can be very small. Current visual recognition algorithms have achieved excellent classification accuracy. However, they require many training examples to reach peak performance, which suggests that long-tailed distributions will not be dealt with well. We analyze this question in the context of eBird, a large fine-grained classification dataset, and a state-of-the-art deep network classification algorithm. We find that (a) peak classification performance on well-represented categories is excellent, (b) given enough data, classification performance suffers only minimally from an increase in the number of classes, (c) classification performance decays precipitously as the number of training examples decreases, (d) surprisingly, transfer learning is virtually absent in current methods. Our findings suggest that our community should come to grips with the question of long tails.

Additional Information

© 2017. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

Attached Files

Submitted - 1709.01450.pdf

Files

1709.01450.pdf
Files (3.1 MB)
Name Size Download all
md5:0da2603ddbabace2aa8b0e692ce5bce8
3.1 MB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 20, 2023