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Spatio-temporal Action Recognition
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hayatkhan8660-maker/DVFL-Net
공식 코드https://github.com/2023-MindSpore-1/ms-code-17/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/2023-MindSpore-4/Code8/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/Mind23-2/MindCode-11
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/MindSpore-paper-code-3/code6/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/SmartPorridge/google-AVA-Dataset-downloader
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/Whiffe/Custom-ava-dataset_Custom-Spatio-Temporally-Action-Video-Dataset
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/open-mmlab/mmaction2
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/pwc-1/Paper-9/tree/main/3/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/tensorflow/models
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.