5 accepted papers
Recognizing subtle similarities and differences among sets of similar activities is central to many real-world applications, including skill acquisition, sports performance evaluation, and anomaly detection. Humans excel at such fine-grained analysis, which requires comprehensive video understanding…
Recognizing daily human activities is essential for domestic robots to assist humans effectively in indoor environments. These activities typically involve sequences of interactions between humans and objects across different locations and times within a household. Identifying these events and under…
Out-of-distribution (OOD) detection, which aims to determine whether an input is outside the training data distribution or not, is an indispensable task in many computer vision applications. In many of the previous studies on OOD detection for image classification, the class-conditional distribution…
Traffic accident recognition is essential in developing automated driving and Advanced Driving Assistant System technologies. A large dataset of annotated traffic accidents is necessary to improve the accuracy of traffic accident recognition using deep learning models. Conventional traffic accident…
The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs in the field of action recognition have improved…