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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | https://arxiv.org/abs/2408.13431 |
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| _version_ | 1866918007794565120 |
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| author | Liu, Junjie |
| author_facet | Liu, Junjie |
| contents | Large-scale face clustering has achieved significant progress, with many efforts dedicated to learning to cluster large-scale faces with supervised-learning. However, complex model design and tedious clustering processes are typical in existing methods. Such limitations result in infeasible clustering in real-world applications. Reasonable and efficient model design and training need to be taken into account. Besides, developing unsupervised face clustering algorithms is crucial, which are more realistic in real-world applications. In this paper, we propose a novel unsupervised face clustering algorithm FC-ES and a novel supervised face clustering algorithm FC-ESER to address these issues. An efficient and effective neighbor-based edge probability and a novel early stopping strategy are proposed in FC-ES, guaranteeing the accuracy and recall of large-scale face clustering simultaneously. Furthermore, to take advantage of supervised learning, a novel edge recall strategy is proposed in FC-ESER to further recall the edge connections that are not connected in FC-ES. Extensive experiments on multiple benchmarks for face, person, and vehicle clustering show that our proposed FC-ES and FC-ESER significantly outperform previous state-of-the-art methods. Our code will be available at https://github.com/jumptoliujj/FC-ESER. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13431 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Face Clustering via Early Stopping and Edge Recall Liu, Junjie Computer Vision and Pattern Recognition Large-scale face clustering has achieved significant progress, with many efforts dedicated to learning to cluster large-scale faces with supervised-learning. However, complex model design and tedious clustering processes are typical in existing methods. Such limitations result in infeasible clustering in real-world applications. Reasonable and efficient model design and training need to be taken into account. Besides, developing unsupervised face clustering algorithms is crucial, which are more realistic in real-world applications. In this paper, we propose a novel unsupervised face clustering algorithm FC-ES and a novel supervised face clustering algorithm FC-ESER to address these issues. An efficient and effective neighbor-based edge probability and a novel early stopping strategy are proposed in FC-ES, guaranteeing the accuracy and recall of large-scale face clustering simultaneously. Furthermore, to take advantage of supervised learning, a novel edge recall strategy is proposed in FC-ESER to further recall the edge connections that are not connected in FC-ES. Extensive experiments on multiple benchmarks for face, person, and vehicle clustering show that our proposed FC-ES and FC-ESER significantly outperform previous state-of-the-art methods. Our code will be available at https://github.com/jumptoliujj/FC-ESER. |
| title | Face Clustering via Early Stopping and Edge Recall |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.13431 |