Large-Scale Long-Tailed Recognition in An Open World

TitleLarge-Scale Long-Tailed Recognition in An Open World
Publication TypeConference Paper
Year of Publication2019
AuthorsLiu, Z., Miao Z., Zhan X., Wang J., Gong B., & Yu S. X.
Published inProceedings of IEEE Conference on Computer Vision and Pattern Recognition
Keywordsfew-shot learning, imbalanced classification, open long-tailed recognition, open-set recognition

Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known instances, and acknowledge novelty upon a never seen instance. We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.

OLTR must handle imbalanced classification, few-shot learning, and open-set recognition in one integrated algorithm, whereas existing classification approaches focus only on one aspect and deliver poorly over the entire class spectrum. The key challenges are how to share visual knowledge between head and tail classes and how to reduce confusion between tail and open classes.

We develop an integrated OLTR algorithm that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world. Our so-called dynamic meta-embedding combines a direct image feature and an associated memory feature, with the feature norm indicating the familiarity to known classes. On three large-scale OLTR datasets we curate from object-centric ImageNet, scene-centric Places, and face-centric MS1M data, our method consistently outperforms the state-of-the-art. Our code, datasets, and models enable future OLTR research and are publicly available at \url{}.


This research was supported, in part, by SenseTime Group Limited, NSF IIS 1835539, Berkeley Deep Drive, DARPA, and US Government fund through Etegent Technologies on Low-Shot Detection in Remote Sensing Imagery. The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

ICSI Research Group