Current research is primarily dedicated to advancing the accuracy of camera-only 3D object
detectors (apprentice) through the knowledge transferred from LiDAR-or multi-modal-based …

With the remarkable progress of deep neural networks in computer vision, data mixing
augmentation techniques are widely studied to alleviate problems of degraded …

Mixup is an efficient data augmentation approach that improves the generalization of neural
networks by smoothing the decision boundary with mixed data. Recently, dynamic mixup …

Mixup data augmentation approaches have been applied for various tasks of deep learning
to improve the generalization ability of deep neural networks. Some existing approaches …

Unsupervised domain adaptation (UDA) plays a crucial role in object detection when
adapting a source-trained detector to a target domain without annotated data. In this paper …

As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data
augmentations have garnered increasing attention as regularization techniques when …

Data mixing, or mixup, is a data-dependent augmentation technique that has greatly
enhanced the generalizability of modern deep neural networks. However, a full grasp of …

In semi-supervised medical image segmentation, the use of CutMix in the Mean Teacher
architecture is considered an effective strong data augmentation strategy. However, we …

In recent years, deep learning (DL) techniques have achieved remarkable success in
various fields of computer vision. This progress was attributed to the vast amounts of data …