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 …

Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Abstract Convolutional Neural Networks (CNN) have been regarded as a powerful class of
models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN …

Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …

The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …

Neural networks trained on datasets such as ImageNet have led to major advances in visual
object classification. One obstacle that prevents networks from reasoning more deeply about …

Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor
triggers in DNNs by poisoning training data. A backdoored model behaves normally on …

Many recent advancements in Computer Vision are attributed to large datasets. Open-
source software packages for Machine Learning and inexpensive commodity hardware …

Video classification is computationally expensive. In this paper, we address theproblem of
frame selection to reduce the computational cost of video classification. Recent work has …