Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

The heavy reliance on data is one of the major reasons that currently limit the development
of deep learning. Data quality directly dominates the effect of deep learning models, and the …

To address the challenges of long-tailed classification, researchers have proposed several
approaches to reduce model bias, most of which assume that classes with few samples are …

Continued improvements in deep learning architectures have steadily advanced the overall
performance of 3D object detectors to levels on par with humans for certain tasks and …

Data privacy and long-tailed distribution are the norms rather than the exception in many
real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task in …

Long-tailed learning aims to tackle the crucial challenge that head classes dominate the
training procedure under severe class imbalance in real-world scenarios. However, little …

How to estimate the uncertainty of a given model is a crucial problem. Current calibration
techniques treat different classes equally and thus implicitly assume that the distribution of …

Modern image classifiers perform well on populated classes while degrading considerably
on tail classes with only a few instances. Humans, by contrast, effortlessly handle the long …

Real-world data typically follows a long-tailed distribution. When a small sample of tail
classes does not cover the underlying distribution well, methods such as class re-balancing …

Abstract Multiple Object Tracking (MOT) is a critical area within computer vision with a broad
spectrum of practical implementations. Current research has primarily focused on the …