Generative Extension Positive Pairs and Improving Sample Selection Based on Contrastive Learning for Unsupervised Person Re-identification | |
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學年 | 112 |
學期 | 2 |
發表日期 | 2024-04-14 |
作品名稱 | Generative Extension Positive Pairs and Improving Sample Selection Based on Contrastive Learning for Unsupervised Person Re-identification |
作品名稱(其他語言) | |
著者 | Zheng-An Zhu; Chen-Kuo Chiang |
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會議名稱 | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing |
會議地點 | Seoul, Korea |
摘要 | In this paper, we present Generative Extension Positive Pairs (GEPP), a novel approach to enhance unsupervised person re-identification (re-id) through contrastive learning. Data generation and pair selection methods significantly impact model performance in contrastive learning. To improve positive pair generation, we incorporate a Generative Adversarial Network (GAN) to create novel views as augmentation samples. We also introduce a sample selection scheme in the contrastive learning process to effectively choose GAN-augmented positive samples. Leveraging our sample selection results, we construct the GEPP framework and propose a unique loss function for contrastive learning. Experimental results showcase that our generative extension of positive pairs and sample selection method offer a versatile, automated, and diverse approach, achieving higher mean average precision (mAP) in re-id tasks than conventional data augmentation techniques. Additionally, our framework outperforms existing state-of-the-art methods on the Market-1501 and MSMT17 datasets. |
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語言 | zh_TW |
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會議性質 | 國內 |
校內研討會地點 | 無 |
研討會時間 | 20240414~20240419 |
通訊作者 | |
國別 | KOR |
公開徵稿 | |
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相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127134 ) |