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

Pei et al., 2019 - Google Patents

Continuous affect recognition with weakly supervised learning

Pei et al., 2019

Document ID
10844962855487289096
Author
Pei E
Jiang D
Alioscha-Perez M
Sahli H
Publication year
Publication venue
Multimedia Tools and Applications

External Links

Snippet

Recognizing a person's affective state from audio-visual signals is an essential capability for intelligent interaction. Insufficient training data and the unreliable labels of affective dimensions (eg, valence and arousal) are two major challenges in continuous affect …
Continue reading at link.springer.com (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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • 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
    • 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
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR

Similar Documents

Publication Publication Date Title
Alam et al. Survey on deep neural networks in speech and vision systems
Tokmakov et al. Learning to segment moving objects
Surís et al. Cross-modal embeddings for video and audio retrieval
Koohzadi et al. Survey on deep learning methods in human action recognition
US20200097818A1 (en) Method and system for training binary quantized weight and activation function for deep neural networks
He et al. Multimodal affective dimension prediction using deep bidirectional long short-term memory recurrent neural networks
Xin et al. Arch: Adaptive recurrent-convolutional hybrid networks for long-term action recognition
Bilal et al. A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes
Pei et al. Continuous affect recognition with weakly supervised learning
Latha et al. A review on deep learning algorithms for speech and facial emotion recognition
CN111859149A (en) Information recommendation method and device, electronic equipment and storage medium
He et al. DepNet: An automated industrial intelligent system using deep learning for video‐based depression analysis
Nida et al. Instructor activity recognition through deep spatiotemporal features and feedforward extreme learning machines
Zong et al. Emotion recognition in the wild via sparse transductive transfer linear discriminant analysis
CN114663798B (en) Single-step video content identification method based on reinforcement learning
Goyal et al. Real-life facial expression recognition systems: a review
CN114140885A (en) Emotion analysis model generation method and device, electronic equipment and storage medium
Rani et al. An effectual classical dance pose estimation and classification system employing convolution neural network–long shortterm memory (CNN-LSTM) network for video sequences
Chauhan et al. Analysis of Intelligent movie recommender system from facial expression
Huttunen Deep neural networks: A signal processing perspective
Zhang et al. End-to-end temporal attention extraction and human action recognition
CN116628345A (en) Content recommendation method and device, electronic equipment and storage medium
Jia et al. Multimodal emotion distribution learning
Munir et al. Background subtraction in videos using LRMF and CWM algorithm
Liu Improved convolutional neural networks for course teaching quality assessment