Real-world data often exhibits long tail distributions with heavy class imbalance, where the
majority classes can dominate the training process and alter the decision boundaries of the …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

In the real open world, data tends to follow long-tailed class distributions, motivating the well-
studied long-tailed recognition (LTR) problem. Naive training produces models that are …

This paper tackles the cross-modality person re-identification (re-ID) problem by
suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images …

Traditional federated optimization methods perform poorly with heterogeneous data (ie,
accuracy reduction), especially for highly skewed data. In this paper, we investigate the label …

Real-world classification problems typically exhibit an imbalanced or long-tailed label
distribution, wherein many labels are associated with only a few samples. This poses a …

As the class size grows, maintaining a balanced dataset across many classes is challenging
because the data are long-tailed in nature; it is even impossible when the sample-of-interest …

Despite the success of the deep neural networks, it remains challenging to effectively build a
system for long-tail visual recognition tasks. To address this problem, we first investigate the …

Deep classifiers have achieved great success in visual recognition. However, real-world
data is long-tailed by nature, leading to the mismatch between training and testing …

Potato disease management plays a valuable role in the agriculture field as it might cause a
significant loss in crops production. Therefore, timely recognition and classification of potato …