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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98913完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王鈺強 | zh_TW |
| dc.contributor.advisor | Yu-Chiang Frank Wang | en |
| dc.contributor.author | 林棋祥 | zh_TW |
| dc.contributor.author | Ci-Siang Lin | en |
| dc.date.accessioned | 2025-08-20T16:15:50Z | - |
| dc.date.available | 2025-08-21 | - |
| dc.date.copyright | 2025-08-20 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-12 | - |
| dc.identifier.citation | [1] J. Ahn, S. Cho, and S. Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In CVPR, 2019.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98913 | - |
| dc.description.abstract | 當前深度學習的快速發展促使多種基礎模型被提出，用以解決視覺與語言的基本任務，而提示學習成為將基礎模型適應下游任務的一種主流微調技術。本論文旨在推進提示學習與選擇技術，以實現高級的視覺分析，包括可解釋的細粒度識別（第 1章）、圖像語義分割（第 2章）以及指向式影片分割（第 3章）。在第 1章中，我們通過學習一組視覺提示，利用視覺轉換器進行注意力機制並提取具辨識性的原型，實現了可解釋的細粒度識別。在第 2章中，我們通過從CLIP模型中學習文本背景提示來提升圖像語義分割效果。最後，在第 3章中，我們的模型能夠根據文本查詢選擇對應的時空提示，從而基於SAM實現指向式影片分割。得益於這些基礎模型所學到的豐富知識，以上任務都能以弱監督方式完成，減少了高昂的標註成本。 | zh_TW |
| dc.description.abstract | With the rapid development of deep learning, several foundation models have been proposed to address fundamental vision and language tasks, and prompt learning becomes a prevalent finetuning technique to adapt foundation models to downstream tasks. In this dissertation, we aim to advance prompt learning and selection techniques to achieve advanced visual analysis, including interpretable fine-grained recognition (Chapter 1), image semantic segmentation (Chapter 2), and referring video segmentation (Chapter 3). In Chapter 1, we achieve interpretable fine-grained recognition by learning a set of visual prompts to perform attention through vision transformer and derive discriminative prototypes. In Chapter 2, we enhance image semantic segmentation by learning textual background prompts from the CLIP model. Lastly, in Chapter 3, our model learns to select desired spatial-temporal prompts corresponding to the text query, addressing referring video segmentation based on SAM. Thanks to the rich knowledge learned inside these foundation models, the above tasks are able to be achieved in a weakly-supervised manner, alleviating expensive annotation costs. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-20T16:15:50Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-20T16:15:50Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 iii Abstract v Contents vii List of Figures xi List of Tables xv Chapter 1 Visual Prompt Learning for Interpretable Fine-grained Recognition 1 1.1 Introduction 2 1.2 Related Works 6 1.2.1 Explainable Artificial Intelligence (XAI) 6 1.2.2 Fine-Grained Recognition 7 1.3 Methodology 8 1.3.1 Notations and Overview 8 1.3.2 Descriptive Prototype Learning 9 1.3.3 Discriminative Prototype Discovery 12 1.4 Experiments 15 1.4.1 Datasets 15 1.4.2 Implementation Details 17 1.4.3 Interpreting Descriptive Prototypes 18 1.4.4 Discriminative Prototypes for Visual Reasoning 22 1.4.5 Ablation Studies 24 1.5 Conclusion 26 Chapter 2 Textual Prompt Learning for Image Semantic Segmentation 29 2.1 Introduction 30 2.2 Related Works 33 2.2.1 Weakly-Supervised Semantic Segmentation 33 2.2.2 CLIP-based Semantic Segmentation 34 2.2.3 Prompt Learning 35 2.3 Proposed Method 36 2.3.1 Problem Formulation and Model Overview 36 2.3.2 Semantic Prompt Learning for WSSS 37 2.3.2.1 Segment-Label Matching 37 2.3.2.2 Contrastive Prompt Learning 38 2.3.2.3 Prompt-guided Semantic Refinement 40 2.4 Experiments 41 2.4.1 Datasets and Evaluation Metrics 41 2.4.2 Implementation Details 41 2.4.3 Quantitative Comparisons 43 2.4.4 Qualitative Comparisons 46 2.4.5 Ablation Studies 47 2.5 Conclusion 49 Chapter 3 Spatial-Temporal Prompt Selection for Referring Video Segmentation 51 3.1 Introduction 52 3.2 Related Works 56 3.2.1 Referring Image/Video Segmentation 56 3.2.2 Foundation Segmentation Models 57 3.3 Proposed Framework: TENET 58 3.3.1 Problem Definition and Method Overview 58 3.3.2 Temporal Prompt Generation and Analysis 60 3.3.3 Prompt Preference Learning 64 3.4 Experiments 66 3.4.1 Datasets and Implementation Details 66 3.4.2 Quantitative and Qualitative Results 66 3.4.3 Ablation Studies 68 3.5 Conclusion 69 Chapter 4 Conclusion 71 References 73 | - |
| dc.language.iso | en | - |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 人工智慧 | zh_TW |
| dc.subject | 影片 | zh_TW |
| dc.subject | 圖像 | zh_TW |
| dc.subject | 電腦視覺 | zh_TW |
| dc.subject | computer vision | en |
| dc.subject | image | en |
| dc.subject | video | en |
| dc.subject | deep learning | en |
| dc.subject | artificial intelligence | en |
| dc.title | 提示學習與選擇於弱監督視覺分析 | zh_TW |
| dc.title | Prompt Learning and Selection for Weakly-Supervised Visual Analysis | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 莊永裕;賴尚宏;邱維辰;陳駿丞 | zh_TW |
| dc.contributor.oralexamcommittee | Yung-Yu Chuang;Shang-Hong Lai;Wei-Chen Chiu;Jun-Cheng Chen | en |
| dc.subject.keyword | 人工智慧,深度學習,電腦視覺,圖像,影片, | zh_TW |
| dc.subject.keyword | artificial intelligence,deep learning,computer vision,image,video, | en |
| dc.relation.page | 99 | - |
| dc.identifier.doi | 10.6342/NTU202504082 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-08-14 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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