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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.05530 |
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| _version_ | 1866911897861750784 |
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| author | Salgado, Erik Isai Valle Li, Chen Han, Yaqi Shi, Linchao Li, Xinghui |
| author_facet | Salgado, Erik Isai Valle Li, Chen Han, Yaqi Shi, Linchao Li, Xinghui |
| contents | Ensemble methods exploit the availability of a given number of classifiers or detectors trained in single or multiple source domains and tasks to address machine learning problems such as domain adaptation or multi-source transfer learning. Existing research measures the domain distance between the sources and the target dataset, trains multiple networks on the same data with different samples per class, or combines predictions from models trained under varied hyperparameters and settings. Their solutions enhanced the performance on small or tail categories but hurt the rest. To this end, we propose a modified consensus focus for semi-supervised and long-tailed object detection. We introduce a voting system based on source confidence that spots the contribution of each model in a consensus, lets the user choose the relevance of each class in the target label space so that it relaxes minority bounding boxes suppression, and combines multiple models' results without discarding the poisonous networks. Our tests on synthetic driving datasets retrieved higher confidence and more accurate bounding boxes than the NMS, soft-NMS, and WBF. The code used to generate the results is available in our GitHub repository: http://github.com/ErikValle/Consensus-focus-for-object-detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05530 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Consensus Focus for Object Detection and minority classes Salgado, Erik Isai Valle Li, Chen Han, Yaqi Shi, Linchao Li, Xinghui Computer Vision and Pattern Recognition Ensemble methods exploit the availability of a given number of classifiers or detectors trained in single or multiple source domains and tasks to address machine learning problems such as domain adaptation or multi-source transfer learning. Existing research measures the domain distance between the sources and the target dataset, trains multiple networks on the same data with different samples per class, or combines predictions from models trained under varied hyperparameters and settings. Their solutions enhanced the performance on small or tail categories but hurt the rest. To this end, we propose a modified consensus focus for semi-supervised and long-tailed object detection. We introduce a voting system based on source confidence that spots the contribution of each model in a consensus, lets the user choose the relevance of each class in the target label space so that it relaxes minority bounding boxes suppression, and combines multiple models' results without discarding the poisonous networks. Our tests on synthetic driving datasets retrieved higher confidence and more accurate bounding boxes than the NMS, soft-NMS, and WBF. The code used to generate the results is available in our GitHub repository: http://github.com/ErikValle/Consensus-focus-for-object-detection. |
| title | Consensus Focus for Object Detection and minority classes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.05530 |