Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition

Song, Yifan; Zhang, Zhang; Shan, Caifeng; Wang, Liang

doi:10.1109/tpami.2022.3157033

Cited by 203 publications

(108 citation statements)

References 57 publications

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“…A step toward this identification could be localization of movement features within skeleton sequences (eFigure 3 in the Supplement ). 40 , 42 In the present study, we did not investigate whether the deep learning method used features associated with fidgety movements, other movements, and postural patterns in the early motor repertoire (eg, kicking and body symmetry) 43 or as yet unidentified patterns of movement.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

et al. 2022

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Key Points Question What is the external validity of a deep learning–based method to predict cerebral palsy (CP) based on infants’ spontaneous movements at 9 to 18 weeks’ corrected age? Findings In this prognostic study of 557 infants with a high risk of perinatal brain injury, a deep learning–based method for early prediction of CP had sensitivity of 71%, specificity of 94%, positive predictive value of 68%, and negative predictive value of 95%. Prognosis of CP based on the deep learning–based method was associated with later functional level and CP subtype in children with CP. Meaning This study’s findings suggest that deep learning–based assessments could support early detection of CP in infants at high risk.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

et al. 2022

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“…Besides, to reduce computational costs of GCNs, a Shift-GCN was designed by Cheng et al [238], which adopts shift graph operations and lightweight point-wise convolutions, instead of using heavy regular graph convolutions. Following this research line, Song et al [239] proposed a multi-stream GCN model, which fuses the input branches including joint positions, motion velocities, and bone features at early stage, and utilized separable convolutional layers and a compound scaling strategy to extremely reduce the redundant trainable parameters while increasing the capacity of model. Different from the above mentioned methods, Li et al [240] proposed symbiotic GCNs to handle both action recognition and motion prediction tasks simultaneously.…”

Section: Gnn or Gcn-based Methodsmentioning

confidence: 99%

Human Action Recognition From Various Data Modalities: A Review

Sun

Rahmani

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

252

135

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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 attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios. Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities. In this paper, we present a comprehensive survey of recent progress in deep learning methods for HAR based on the type of input data modality. Specifically, we review the current mainstream deep learning methods for single data modalities and multiple data modalities, including the fusion-based and the co-learning-based frameworks. We also present comparative results on several benchmark datasets for HAR, together with insightful observations and inspiring future research directions.

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“…From Table 1 , we evaluated four most relevant state-of-the-art human activity recognition models in the last three years (Efficient GCN [14] , CTR-GCN [7] , MS-G3D [1] , 2S-AGCN [3] ) on the new POLIMI-ITW-S dataset. We believe the results are representative for the current mainstream human activity recognition algorithms.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%