Multi-Proxy Loss:基於度量學習提出之損失函數用於細粒度圖像檢索

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林宛儀(Wan Yi Lin) 查詢紙本館藏

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資訊工程學系

論文名稱

Multi-Proxy Loss:基於度量學習提出之損失函數用於細粒度圖像檢索
(Multi-Proxy Loss: For Deep Metric Learning on Fine-grained Image Retrieval)

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摘要(中)

本篇論文針對圖像檢索(Image retrieval )的任務上提出了一個新的損失函數。此方法基於Proxy_NCA以及Proxy_Anchor的方法上加上了多個代表點的方法，來提升樣本的豐富性。使得Batch size減少的情況下也能達到跟原來較大的batch size一樣的效果。並且使用SoftMax函數對類內代表點做加權。使得重要的代表點能得到更多的學習資源。除此損失函數地改良之外，也對現有的ResNet50進行了修改，只使用RestNet50的前三層做為特徵擷取，取消了ResNet50第三層的下採樣。並且加入了Attention機制取代原本ResNet50的第四層。Attention使用了SoftPlus函數對特徵圖的特徵做加權。使得重要的特徵能更明顯，不重要的特徵減少關注度。相較於傳統Attention使用SoftMax函數能得到更好的效果。不管是新提出的損失函數，或是改良過後的ResNet50都相較於原始方法Recall@1都有很大的提升。

摘要(英)

In this paper, we propose a new loss function for Image Retrieval task. The new loss function makes an improvement based on Proxy-NCA and Proxy-Anchor Loss by adopting multiple proxies, to promote positive sample variety. Its shows better performance than Proxy-Anchor Loss even in the small batch size. Besides, we weighted intra-class proxy by SoftMax function to make important samples receive a higher gradient while training. In addition, we make some changes on ResNet50 by only using the first three-layer and adding a new attention module by using SoftPlus function to replace SoftMax. Finally, we obtain well results on recall@1 via our new method.

關鍵字(中)

★ 度量學習
★ 距離學習
★ 圖像檢索
★ 細粒度圖像
★ 卷積神經網路

關鍵字(英)

★ Deep Metric Learning
★ Distance metric learning
★ Image Retrieval
★ Fine-grained
★ Convention Network

論文目次

摘要 I
ABSTRACT II
目錄 III
圖目錄 V
表目錄 VI
第一章簡介 1
第二章相關研究 3
2-1 DEEP METRIC LEARNING 3
2-1-1 Triplet 3
2-1-2 Proxy-NCA 5
2-1-3 SoftTriple 7
2-1-4 Multi-Similarity 7
2-1-5 Proxy-Anchor 9
2-2 卷積神經網路 11
2-2-1 BN-inception 11
2-2-2 ResNet50 12
2-2-3 Ensemble learning 13
第三章研究方法 14
3-1 LOSS FUNCTION設計 15
3-2 網路架構 19
第四章實驗結果 22
4-1實驗設置 22
4-2資料集介紹 22
4-2-1 Stanford Cars Datasets(CARS196) 22
4-2-2 Caltech-UCSD Birds-200-2011 (CUB-200-2011) 23
4-3實驗測量指標 24
4-4實驗結果 25
第五章結論與未來工作 32
參考文獻: 33

參考文獻

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指導教授

范國清韓欽銓(Kuo-Chin Fan Chin-Chuan Han)

審核日期

2022-7-21

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