Human action recognition from various data modalities: A review
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 …
each action. It has a wide range of applications, and therefore has been attracting increasing …
Artificial intelligence in physical sciences: Symbolic regression trends and perspectives
D Angelis, F Sofos, TE Karakasidis - Archives of Computational Methods …, 2023 - Springer
Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …
programming principles that integrates techniques and processes from heterogeneous …
Human action recognition and prediction: A survey
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 …
have been moving from inferring the present state to predicting the future state. Vision-based …
Learning spatio-temporal representation with pseudo-3d residual networks
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 …
models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN …
Deep multimodal representation learning: A survey
W Guo, J Wang, S Wang - Ieee Access, 2019 - ieeexplore.ieee.org
Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …
Deep multimodal learning: A survey on recent advances and trends
D Ramachandram, GW Taylor - IEEE signal processing …, 2017 - ieeexplore.ieee.org
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 …
learning problems, which often involve multiple data modalities. We review recent advances …
The" something something" video database for learning and evaluating visual common sense
R Goyal, S Ebrahimi Kahou… - Proceedings of the …, 2017 - openaccess.thecvf.com
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 …
object classification. One obstacle that prevents networks from reasoning more deeply about …
Clean-label backdoor attacks on video recognition models
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 …
triggers in DNNs by poisoning training data. A backdoored model behaves normally on …
Youtube-8m: A large-scale video classification benchmark
Many recent advancements in Computer Vision are attributed to large datasets. Open-
source software packages for Machine Learning and inexpensive commodity hardware …
source software packages for Machine Learning and inexpensive commodity hardware …
Smart frame selection for action recognition
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 …
frame selection to reduce the computational cost of video classification. Recent work has …