Several image processing tasks, such as image classification and object detection, have
been significantly improved using Convolutional Neural Networks (CNN). Like ResNet and …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

The YOLO series has become the most popular framework for real-time object detection due
to its reasonable trade-off between speed and accuracy. However we observe that the …

We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO
series and is easily extensible for many object recognition tasks such as instance …

Recognition in low quality face datasets is challenging because facial attributes are
obscured and degraded. Advances in margin-based loss functions have resulted in …

Considering the ill-posed nature, contrastive regularization has been developed for single
image dehazing, introducing the information from negative images as a lower bound …

Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input
source: the feature, the logit, or the softmax probability. However, the immense diversity of …

Recent studies show that Vision Transformers (ViTs) exhibit strong robustness against
various corruptions. Although this property is partly attributed to the self-attention …