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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳沛遠(Pei-Yuan Wu) | |
| dc.contributor.author | Po-Min Hsu | en |
| dc.contributor.author | 許博閔 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:36:15Z | - |
| dc.date.available | 2020-08-21 | |
| dc.date.copyright | 2020-08-21 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-07 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60916 | - |
| dc.description.abstract | 在本文中，我們提出了ODVA用於單樣本物件檢測，其中要檢測的目標類別可以是訓練數據集中是沒見過的。 我們的ODVA使用可見類中的圖像進行訓練，而在推論階段中，ODVA會在查詢圖像中檢測與給定支持圖像匹配的對象，包含見過或沒見過的類別且無需進行任何模型微調。借助空間和通道注意力，對支持圖像中的可區分特徵進行編碼，並估算查詢圖像和支持圖像之間的相似度。 從中，基於餘量的損失函數旨在指導ODVA學習針對沒見過類別的合適度量方法。 對VOC和MS-COCO數據集的實驗評估表明，與其他最新的單樣本和元學習文獻相比，本文提出的ODVA是有效的。 此外，為了支持可解釋性，我們將RPN提案區域和注意力向量可視化，並通過消融研究證明ODVA中每個模塊的有效性。 | zh_TW |
| dc.description.abstract | In this thesis, we propose ODVA for one-shot object detection, in which the object to be detected can be unseen in the training dataset. Our ODVA is trained with images in the seen classes, while in the inference phase ODVA detects objects in the query image that match a given support image containing an unseen class without any fine-tuning. With the help of spatial and channel attentions, distinguishable features in the support image are encoded and the similarity between query and support images is estimated. From which, a margin-based loss is designed to guide ODVA into learning an appropriate metric for the unseen classes. Experimental evaluations on both VOC and MS-COCO datasets show the effectiveness of the proposed ODVA compared to other start-of-the-art one-shot and meta-learning works. In addition, to favor interpretability, we visualize the RPN proposals and attention vectors, and demonstrate the effectiveness of each module in ODVA through ablation study. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:36:15Z (GMT). No. of bitstreams: 1 U0001-0107202022242500.pdf: 15380021 bytes, checksum: 8767200cf862d1fd03cbeee52966b9f8 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | Acknowledgements i 摘要 ii Abstract iii Contents iv List of Figures vi List of Tables viii Chapter 1 Introduction 1 Chapter 2 Related work 5 Chapter 3 Proposed method 8 3.1 Problem definition 8 3.2 Model formulation 9 3.3 Spatial attention module 10 3.4 Channel attention module 12 3.5 Loss function 13 Chapter 4 Experiments 16 4.1 Datasets 16 4.2 Implementation detail 17 4.3 Existing benchmarks 18 4.4 Revised evaluation protocol 19 4.5 Cross domain detection 21 Chapter 5 Discussions 23 5.1 Visualizing RPN proposals 23 5.2 Visualizing the characteristics of channel attention 23 5.3 Performance over multiple runs 25 5.4 Ablation study 27 5.5 Pooling method and fusion method 30 5.6 Number of parameters and running time 31 Chapter 6 Conclusion 32 References 34 | |
| dc.language.iso | en | |
| dc.title | 單樣本物件檢測藉由多功能注意力機制 | zh_TW |
| dc.title | One-Shot Object Detection Using Versatile Attentions | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 丁建均(Jian-Jyun Ding),王鈺強(Yu-Ciang Wang),林昌鴻(Chang-Hong Lin) | |
| dc.subject.keyword | 物件檢測,深度學習,單樣本學習,注意力模型,度量學習, | zh_TW |
| dc.subject.keyword | Object detection,deep learning,one-shot learning,attention model,metric learning, | en |
| dc.relation.page | 40 | |
| dc.identifier.doi | 10.6342/NTU202001254 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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