Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Target encoding plays a central role when learning Convolutional Neural Networks. In this
realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so …

Clearly explaining a rationale for a classification decision to an end user can be as important
as the decision itself. Existing approaches for deep visual recognition are generally opaque …

Compared to earlier multistage frameworks using CNN features, recent end-to-end deep
approaches for fine-grained recognition essentially enhance the mid-level learning …

Conventional finger-vein recognition systems perform recognition based on the finger-vein
lines extracted from the input images or image enhancement, and texture feature extraction …

Convolutional neural networks (CNNs) have been successfully used in a range of tasks.
However, CNNs are often viewed as “black-box” and lack of interpretability. One main …

We had previously proposed a supervised Laplacian eigenmap for visualization (SLE-ML)
that can handle multi-label data. In addition, SLE-ML can control the trade-off between the …

While the action recognition task on videos collected from visible spectrum imaging has
received much attention, action recognition in infrared (IR) videos is significantly less …

Explainability and interpretability of AI models is an essential factor affecting the safety of AI.
While various explainable AI (XAI) approaches aim at mitigating the lack of transparency in …

Human action recognition (HAR) is an important area of research in the field of computer
vision. Though a lot of efforts have been made in the past for HAR in visible spectrum, yet …