Nothing Special   »   [go: up one dir, main page]

Zhang et al., 2022 - Google Patents

Semi-supervised action quality assessment with self-supervised segment feature recovery

Zhang et al., 2022

Document ID
1321737666304412176
Author
Zhang S
Pan J
Gao J
Zheng W
Publication year
Publication venue
IEEE Transactions on Circuits and Systems for Video Technology

External Links

Snippet

Action Quality Assessment aims to evaluate how well an action performs. Existing methods have achieved remarkable progress on fully-supervised action assessment. However, in real-world applications, with expert's experience, it is not always feasible to manually label …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268—Feature extraction; Face representation
    • G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00288—Classification, e.g. identification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Xu et al. X-invariant contrastive augmentation and representation learning for semi-supervised skeleton-based action recognition
Zhang et al. Semi-supervised action quality assessment with self-supervised segment feature recovery
Dai et al. Human action recognition using two-stream attention based LSTM networks
Shu et al. Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition
Gao et al. Domain-adaptive crowd counting via high-quality image translation and density reconstruction
Simonyan et al. Two-stream convolutional networks for action recognition in videos
Meng et al. Sample fusion network: An end-to-end data augmentation network for skeleton-based human action recognition
Das et al. Where to focus on for human action recognition?
Rothfuss et al. Deep episodic memory: Encoding, recalling, and predicting episodic experiences for robot action execution
CN110575663B (en) Physical education auxiliary training method based on artificial intelligence
Gao et al. A novel multiple-view adversarial learning network for unsupervised domain adaptation action recognition
Yan et al. Predicting human interaction via relative attention model
Chappa et al. Sogar: Self-supervised spatiotemporal attention-based social group activity recognition
Hosu et al. AIM 2024 challenge on uhd blind photo quality assessment
CN113205043B (en) Video sequence two-dimensional attitude estimation method based on reinforcement learning
Liu et al. Key algorithm for human motion recognition in virtual reality video sequences based on hidden markov model
Bai et al. Double chain networks for monocular 3D human pose estimation
Liu et al. Solution of wide and micro background bias in contrastive action representation learning
Liu et al. A knowledge-based hierarchical causal inference network for video action recognition
Ma et al. Human pose estimation based on gan and dcgan models
Pang et al. Target tracking based on siamese convolution neural networks
CN111178141B (en) LSTM human body behavior identification method based on attention mechanism
Liu et al. Neural network for intelligent and efficient volleyball passing training
Wu et al. Capturing implicit spatial cues for monocular 3D hand reconstruction
Liu et al. Action Jitter Killer: Joint Noise Optimization Cascade for Skeleton-based Action Recognition