Abstract
Mohammed El Jattioui, Hamid Tairi, Wiem Takrouni
Abstract
Authors
Institutions
Provenance
crossref
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Unresolved referenced work
2025
10.1109/niles63360.2024.10753192
10.1109/niles63360.2024.10753192
Unresolved referenced work
1998
Evaluating YouTube videos for young children
10.1007/s10639-020-10183-7 · 2020
10.1145/3297280.3297487
10.1145/3297280.3297487
Unresolved referenced work
2025
10.1609/icwsm.v17i1.22209
10.1609/icwsm.v17i1.22209
Unresolved referenced work
2025
Wav2vec 2.0: A framework for self-supervised learning of speech representations
2020
Enhanced multimodal content moderation of children’s videos using audiovisual fusion
2024
SAFEPLAY-X: A comprehensive gameplay video dataset for violence detection with explainable deep learning applications
10.1016/j.eswa.2026.131724 · 2026
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
Is space–time attention all you need for video understanding?
2021
Unresolved referenced work
2020
A deep learning-based approach for inappropriate content detection and classification of YouTube videos
10.1109/access.2022.3147519 · 2022
Using computer vision to detect e-cigarette content in TikTok videos
10.1093/ntr/ntad184 · 2024
VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training
10.52202/068431-0732 · 2022
10.1109/iccv48922.2021.00676
10.1109/iccv48922.2021.00676
10.1145/3746027.3754558
10.1145/3746027.3754558
10.21437/interspeech.2021-698
10.21437/interspeech.2021-698
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
2022
Unresolved referenced work
2020
10.1109/icassp43922.2022.9747631
10.1109/icassp43922.2022.9747631
Unresolved referenced work
2019
Unresolved referenced work
2016
Malicious or benign? Towards effective content moderation for children’s videos
2023
Unresolved referenced work
2019
Super-convergence: Very fast training of neural networks using large learning rates
2019
10.1109/iccv.2017.74
10.1109/iccv.2017.74
Unresolved referenced work
2016
10.1109/iccv.2017.74
10.1109/iccv.2017.74 · doi-reference
10.1109/icassp43922.2022.9747631
10.1109/icassp43922.2022.9747631 · doi-reference
10.21437/interspeech.2021-698
10.21437/interspeech.2021-698 · doi-reference
10.1145/3746027.3754558
10.1145/3746027.3754558 · doi-reference
10.1109/iccv48922.2021.00676
10.1109/iccv48922.2021.00676 · doi-reference
VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training
10.52202/068431-0732 · doi-reference
Using computer vision to detect e-cigarette content in TikTok videos
10.1093/ntr/ntad184 · doi-reference
A deep learning-based approach for inappropriate content detection and classification of YouTube videos
10.1109/access.2022.3147519 · doi-reference
SAFEPLAY-X: A comprehensive gameplay video dataset for violence detection with explainable deep learning applications
10.1016/j.eswa.2026.131724 · doi-reference
10.1609/icwsm.v17i1.22209
10.1609/icwsm.v17i1.22209 · doi-reference
10.1145/3297280.3297487
10.1145/3297280.3297487 · doi-reference
Evaluating YouTube videos for young children
10.1007/s10639-020-10183-7 · doi-reference
10.1109/niles63360.2024.10753192
10.1109/niles63360.2024.10753192 · doi-reference