“CN-Celeb”版本间的差异
来自cslt Wiki
| 第44行： | 第44行： | ||
* Wang et al., "CosFace: Large Margin Cosine Loss for Deep Face Recognition", 2018, [https://arxiv.org/pdf/1801.09414.pdf] | * Wang et al., "CosFace: Large Margin Cosine Loss for Deep Face Recognition", 2018, [https://arxiv.org/pdf/1801.09414.pdf] | ||
* Liu et al., "SphereFace: Deep Hypersphere Embedding for Face Recognition", 2017[https://arxiv.org/pdf/1704.08063.pdf] | * Liu et al., "SphereFace: Deep Hypersphere Embedding for Face Recognition", 2017[https://arxiv.org/pdf/1704.08063.pdf] | ||
| − | * Zhong et al., "GhostVLAD for set-based face recognition", 2018. [http://www.robots.ox.ac.uk/~vgg/publications/2018/Zhong18b/zhong18b.pdf | + | * Zhong et al., "GhostVLAD for set-based face recognition", 2018. [http://www.robots.ox.ac.uk/~vgg/publications/2018/Zhong18b/zhong18b.pdf] |
| − | * Chung et al., "Out of time: automated lip sync in the wild", 2016.[http://www.robots.ox.ac.uk/~vgg/publications/2016/Chung16a/chung16a.pdf | + | * Chung et al., "Out of time: automated lip sync in the wild", 2016.[http://www.robots.ox.ac.uk/~vgg/publications/2016/Chung16a/chung16a.pdf] |
| − | * Xie et al., "Utterance-level Aggregation For Speaker Recognition In The Wild", 2019. [https://arxiv.org/pdf/1902.10107.pdf | + | * Xie et al., "Utterance-level Aggregation For Speaker Recognition In The Wild", 2019. [https://arxiv.org/pdf/1902.10107.pdf] |
| − | * Zhang1 et al., "Fully Supervised Speaker Diarization", 2018. [https://arxiv.org/pdf/1810.04719v1.pdf | + | * Zhang1 et al., "Fully Supervised Speaker Diarization", 2018. [https://arxiv.org/pdf/1810.04719v1.pdf] |
2019年10月29日 (二) 12:15的版本
目录
Introduction
- CN-Celeb, a large-scale Chinese celebrities dataset collected `in the wild'.
Members
- Current：Dong Wang, Yunqi Cai, Lantian Li, Yue Fan, Jiawen Kang
- History：Ziya Zhou, Kaicheng Li, Haolin Chen, Sitong Cheng, Pengyuan Zhang
Target
- Collect audio data of 1,000 Chinese celebrities.
- Automatically clip videoes through a pipeline including face detection, face recognition, speaker validation and speaker diarization.
- Create a benchmark database for speaker recognition community.
Future Plans
- Augment the database to 10,000 people.
- Build a model between SyncNet and Speaker_Diarization based on LSTM, which can learn the relationship of them.
Basic Methods
- Environments: Tensorflow, PyTorch, Keras, MxNet
- Face detection and tracking: RetinaFace and ArcFace models.
- Active speaker verification: SyncNet model.
- Speaker diarization: UIS-RNN model.
- Double check by speaker recognition: VGG model.
- Input: pictures and videos of POIs (Persons of Interest).
- Output: well-labelled videos of POIs (Persons of Interest).
GitHub of This Project
Reports
Download
Publications
References
- Deng et al., "RetinaFace: Single-stage Dense Face Localisation in the Wild", 2019. [1]
- Deng et al., "ArcFace: Additive Angular Margin Loss for Deep Face Recognition", 2018, [2]
- Wang et al., "CosFace: Large Margin Cosine Loss for Deep Face Recognition", 2018, [3]
- Liu et al., "SphereFace: Deep Hypersphere Embedding for Face Recognition", 2017[4]
- Zhong et al., "GhostVLAD for set-based face recognition", 2018. [5]
- Chung et al., "Out of time: automated lip sync in the wild", 2016.[6]
- Xie et al., "Utterance-level Aggregation For Speaker Recognition In The Wild", 2019. [7]
- Zhang1 et al., "Fully Supervised Speaker Diarization", 2018. [8]
