3D Human Action Recognition: Through the eyes of researchers
Abstract Human Action Recognition (HAR) has remained one of the most challenging tasks
in computer vision. With the surge in data-driven methodologies, the depth modality has …
in computer vision. With the surge in data-driven methodologies, the depth modality has …
Skeleton-based action recognition with directed graph neural networks
The skeleton data have been widely used for the action recognition tasks since they can
robustly accommodate dynamic circumstances and complex backgrounds. In existing …
robustly accommodate dynamic circumstances and complex backgrounds. In existing …
Skeleton-based action recognition with multi-stream adaptive graph convolutional networks
Graph convolutional networks (GCNs), which generalize CNNs to more generic non-
Euclidean structures, have achieved remarkable performance for skeleton-based action …
Euclidean structures, have achieved remarkable performance for skeleton-based action …
A union of deep learning and swarm-based optimization for 3D human action recognition
Abstract Human Action Recognition (HAR) is a popular area of research in computer vision
due to its wide range of applications such as surveillance, health care, and gaming, etc …
due to its wide range of applications such as surveillance, health care, and gaming, etc …
Two-stream adaptive graph convolutional networks for skeleton-based action recognition
In skeleton-based action recognition, graph convolutional networks (GCNs), which model
the human body skeletons as spatiotemporal graphs, have achieved remarkable …
the human body skeletons as spatiotemporal graphs, have achieved remarkable …
Semantics-guided neural networks for efficient skeleton-based human action recognition
Skeleton-based human action recognition has attracted great interest thanks to the easy
accessibility of the human skeleton data. Recently, there is a trend of using very deep …
accessibility of the human skeleton data. Recently, there is a trend of using very deep …
RGB-D sensing based human action and interaction analysis: A survey
Human activity recognition has been actively studied in the last three decades. Compared to
human action performed by a single person, human interaction is more complex due to the …
human action performed by a single person, human interaction is more complex due to the …
Decoupled spatial-temporal attention network for skeleton-based action-gesture recognition
Dynamic skeletal data, represented as the 2D/3D coordinates of human joints, has been
widely studied for human action recognition due to its high-level semantic information and …
widely studied for human action recognition due to its high-level semantic information and …
Learning spatiotemporal embedding with gated convolutional recurrent networks for translation initiation site prediction
W Li, Y Guo, B Wang, B Yang - Pattern Recognition, 2023 - Elsevier
Accurately predicting translation initiation sites (TIS) from genomic sequences is crucial for
understanding gene regulation and function. TIS prediction methods' feature vectors are not …
understanding gene regulation and function. TIS prediction methods' feature vectors are not …
Context-aware poly (a) signal prediction model via deep spatial–temporal neural networks
Polyadenylation [Poly (A)] is an essential process during messenger RNA (mRNA)
maturation in biological eukaryote systems. Identifying Poly (A) signals (PASs) from the …
maturation in biological eukaryote systems. Identifying Poly (A) signals (PASs) from the …