DANet: Semi-supervised differentiated auxiliaries guided network for video action recognition.

Gao, Guangyu; Liu, Ziming; Zhang, Guangjun; Li, Jinyang; Qin, A K

Gao, Guangyu; Liu, Ziming; Zhang, Guangjun; Li, Jinyang; Qin, A K.

Afiliação

Gao G; School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China. Electronic address: guangyugao@bit.edu.cn.
Liu Z; ACENTAURI team, INRIA; 3IA institute, Université Côte d'Azur, Sophia Antipolis 06902, France.
Zhang G; School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Li J; School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Qin AK; Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia. Electronic address: kqin@swin.edu.au.

Neural Netw ; 158: 121-131, 2023 Jan.

Article em En | MEDLINE | ID: mdl-36455427

Assuntos

Benchmarking; Conhecimento; Humanos; Entropia; Reconhecimento Psicológico; Aprendizado de Máquina Supervisionado

Palavras-chave

Action recognition; Contrastive loss; Semi-supervised learning; Unannotated video

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Conhecimento / Benchmarking Tipo de estudo: Guideline Limite: Humans Idioma: En Revista: Neural Netw Assunto da revista: NEUROLOGIA Ano de publicação: 2023 Tipo de documento: Article

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