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 …

In the real world, large-scale datasets for visual recognition typically exhibit a long-tailed
distribution, where only a few classes contain adequate samples but the others have (much) …

Real-world data typically follow a long-tailed distribution, where a few majority categories
occupy most of the data while most minority categories contain a limited number of samples …

Deep learning systems typically suffer from catastrophic forgetting of past knowledge when
acquiring new skills continually. In this paper, we emphasize two dilemmas, representation …

The problem of class imbalanced data is that the generalization performance of the classifier
deteriorates due to the lack of data from minority classes. In this paper, we propose a novel …

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 …

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

Fusing infrared and visible images can provide more texture details for subsequent object
detection task. Conversely, detection task furnishes object semantic information to improve …

Despite the success of deep neural networks, there are still many challenges in deep
representation learning due to the data scarcity issues such as data imbalance, unseen …

Image-text matching, a bridge connecting image and language, is an important task, which
generally learns a holistic cross-modal embedding to achieve a high-quality semantic …