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Skeletonmae: graph-based masked autoencoder for skeleton sequence pre-training
Skeleton sequence representation learning has shown great advantages for action
recognition due to its promising ability to model human joints and topology. However, the …
recognition due to its promising ability to model human joints and topology. However, the …
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 …
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 …
Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition
J Wang, C Xu, Z Yang, J Zhang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Defect pattern recognition (DPR) of wafer maps is critical for determining the root cause of
production defects, which can provide insights for the yield improvement in wafer foundries …
production defects, which can provide insights for the yield improvement in wafer foundries …
Global spatio-temporal synergistic topology learning for skeleton-based action recognition
Compared to RGB video-based action recognition, skeleton-based action recognition
algorithm has attracted much more attention due to being more lightweight, better …
algorithm has attracted much more attention due to being more lightweight, better …
AdaSGN: Adapting joint number and model size for efficient skeleton-based action recognition
Existing methods for skeleton-based action recognition mainly focus on improving the
recognition accuracy, whereas the efficiency of the model is rarely considered. Recently …
recognition accuracy, whereas the efficiency of the model is rarely considered. Recently …
Multi-scale sampling attention graph convolutional networks for skeleton-based action recognition
H Tian, Y Zhang, H Wu, X Ma, Y Li - Neurocomputing, 2024 - Elsevier
Skeleton-based action recognition has attracted increasing interest in recent years. With the
flexibility of modeling long-range dependency of joints, the self-attention module has served …
flexibility of modeling long-range dependency of joints, the self-attention module has served …
Iip-transformer: Intra-inter-part transformer for skeleton-based action recognition
Q Wang, S Shi, J He, J Peng, T Liu… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Recently, body part as an intuitive movement unit has received increasing attention in
skeleton-based action. However, the part-level embedding is hard to be fully exploited …
skeleton-based action. However, the part-level embedding is hard to be fully exploited …
[HTML][HTML] Dynamic hand gesture recognition for smart lifecare routines via K-Ary tree hashing classifier
H Ansar, A Ksibi, A Jalal, M Shorfuzzaman… - Applied Sciences, 2022 - mdpi.com
Featured Application The proposed system is an image processing module that monitors,
tracks, and recognizes hand gestures and has been evaluated over publicly available …
tracks, and recognizes hand gestures and has been evaluated over publicly available …
TMMF: Temporal multi-modal fusion for single-stage continuous gesture recognition
H Gammulle, S Denman, S Sridharan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Gesture recognition is a much studied research area which has myriad real-world
applications including robotics and human-machine interaction. Current gesture recognition …
applications including robotics and human-machine interaction. Current gesture recognition …