Journal ArticleDOI
Learning and fusing multiple hidden substages for action quality assessment
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TLDR
In this article, a learning and fusion network of multiple hidden substages is proposed to assess athletic performance by segmenting videos into five substages by a temporal semantic segmentation, and a fully-connected-network-based hidden regression model is built to predict the score of each substage, fusing these scores into the overall score.Citations
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Journal ArticleDOI
Functional movement screen dataset collected with two Azure Kinect depth sensors
TL;DR: In this article , a dataset for vision-based autonomous functional movement screen (FMS) is presented from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up and rotary stability.
Journal ArticleDOI
Functional movement screen dataset collected with two Azure Kinect depth sensors
TL;DR: In this paper , a dataset for vision-based autonomous functional movement screen (FMS) is presented from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up and rotary stability.
Journal ArticleDOI
Skeleton-based deep pose feature learning for action quality assessment on figure skating videos
TL;DR: Wang et al. as mentioned in this paper proposed a skeleton-based deep pose feature learning method to automatically evaluate the complicated activities in long-duration sports videos, such as figure skating and artistic gymnastic.
Book ChapterDOI
Pairwise Contrastive Learning Network for Action Quality Assessment
TL;DR: Wang et al. as mentioned in this paper proposed a pairwise contrastive learning network (PCLN) to address the subtle and critical difference between videos and form an end-to-end AQA model with basic regression network.
Journal ArticleDOI
Gaussian guided frame sequence encoder network for action quality assessment
TL;DR: Wang et al. as mentioned in this paper proposed a Gaussian guided frame sequence encoder network for action quality assessment (AQA), where the image feature of each video frame is extracted by ResNet model.
References
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Journal ArticleDOI
Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.
Proceedings ArticleDOI
Learning Spatiotemporal Features with 3D Convolutional Networks
TL;DR: The learned features, namely C3D (Convolutional 3D), with a simple linear classifier outperform state-of-the-art methods on 4 different benchmarks and are comparable with current best methods on the other 2 benchmarks.
Proceedings ArticleDOI
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Joao Carreira,Andrew Zisserman +1 more
TL;DR: In this article, a Two-Stream Inflated 3D ConvNet (I3D) is proposed to learn seamless spatio-temporal feature extractors from video while leveraging successful ImageNet architecture designs and their parameters.
Posted Content
Two-Stream Convolutional Networks for Action Recognition in Videos
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: Simonyan et al. as discussed by the authors proposed a two-stream ConvNet architecture which incorporates spatial and temporal networks, and demonstrated that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data.
Posted Content
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
TL;DR: Temporal Segment Network (TSN) as discussed by the authors is based on the idea of long-range temporal structure modeling and combines a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video.