A modern deep neural network (DNN) for image classification tasks typically consists of two
parts: a backbone for feature extraction, and a head for feature encoding and class …

Computer vision models normally witness degraded performance when deployed in real-
world scenarios due to unexpected changes in inputs that were not accounted for during …

Data augmentation, a cornerstone technique in deep learning, is crucial in enhancing model
performance, especially with scarce labeled data. While traditional techniques are effective …

Data augmentation is a dominant method for reducing model overfitting and improving
generalization. Most existing data augmentation methods tend to find a compromise in …

While recent progress in video-text retrieval has been driven by the exploration of powerful
model architectures and training strategies, the representation learning ability of video-text …

This work investigates the impact of data augmentation on confidence calibration and
uncertainty estimation in Named Entity Recognition (NER) tasks. For the future advance of …

We present a soft benchmark for calibrating facial expression recognition (FER). While prior
works have focused on identifying affective states, we find that FER models are uncalibrated …

Large occlusions result in a significant decline in image classification accuracy. During
inference, diverse types of unseen occlusions introduce out-of-distribution data to the …

The purpose of this work is to study the behavior of fundamentally different architectures of
neural networks, and to find out whether Transformers can outperform deep Convolutional …

ResNets are currently the mainstream networks used in engineering and research.
However, cellular neural networks have inherent flaws, including typical problems such as …