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研究生:許洸睿
研究生(外文):Kuang-Jui Hsu
論文名稱:弱監督式卷積神經網路學習於視覺顯著物體發掘
論文名稱(外文):Visual Attention-getting Object Discovery via Learning Weakly Supervised CNNs
指導教授:莊永裕林彥宇林彥宇引用關係
指導教授(外文):Yung-Yu ChuangYen-Yu Lin
口試委員:廖弘源賴尚宏陳祝嵩陳駿丞王鈺強陳煥宗孫民
口試委員(外文):Mark LiaoShang-Hong LaiChu-Song ChenJun-Cheng ChenYu-Chiang Frank WangHwann-Tzong ChenMin Sun
口試日期:2019-07-02
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:113
中文關鍵詞:顯著性物體偵測物體共顯著偵測物體協同切割卷積神經網路非監督式學習弱監督式學習
DOI:10.6342/NTU201901212
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在影像處理跟電腦視覺領域中,視覺顯著物體發掘是一個重要的且待解決的難題。這一個議題的主要目標是產生機率圖,用來標示影像中所有吸引人的區域。因為這個任務所產生的結果可以用來指出物體位置跟過濾不相關的背景,因此這個任務對於其他應用,如圖像重定位,視覺追蹤,物體切割跟辨認,是非常重要的。由於卷積神經網路可以有效果地學習影像特徵跟非線性分類器,因此目前的最佳方法都是建構在卷積神經網路上。不過,最大的缺點在於需要大量人工標記的像素等級的訓練資料訓練卷積神經網路。不過由於蒐集這類的資料需要花費量的人力資源,因此會限縮此類任務在其他應用的可能性。

這個博士論文中,我們提出四個方法處理上述所遇到的問題。第一個方法中,我們把類別特定的資訊加入視覺顯著物體發掘任務中,並且提出一個弱監督學習的方法來減少所需的標記成本。所提出的方法包含兩個以卷積神經網路為基的模組,影像等級的分類器跟像素等級的產生器,外加上四個損失函數。結果證明所提出的方法超越目前的弱監督跟強監督方法。第二個方法中,我們提出一個非監督式端對端訓練的方法叫做共注視卷積神經網路用於 物體協同切割,且共注視卷積神經網路可以有效地獲得不同影像間物體一致性資訊,因此所提出的方法可以有效地超越目前的監督跟非監督式最佳方法。第三個方法中,我們提出一個新的卷積神經網路可以一起維持多張影像間物體間的一致性跟單張影像內物體的顯著性於物體共顯著偵測。所提出的方法超越最佳非監督式方法,也跟最佳監督式方法達到相同的效果。最後一個方法中,我們提出一個新的但困難的任務叫做實例等級物體協同切割,並針對這個問題,提出一個共尖峰的概念用於定位且切割不同影像間的物體實例。我們利用共尖峰的概念發展一個簡單且有效的卷積神經網路為基架構用於這一個新的任務。我們針對這個新任務蒐集四個資料集,且在這四個資料集上,我們所提出的架構達到最佳效果。
Visual attention-getting object discovery has been an active topic in the fields of image processing and computer vision for decades. In this topic, the goal is to produce the saliency maps which highlights the regions of objects attracting people. This task is crucial to various applications such as image retargeting, visual tracking, object segmentation, and object recognition because the produced results can indicate objects of interest and mask out the irrelevant background. The current state-of-the-art saliency detection methods adopt convolutional neural networks (CNNs) because it has demonstrated effectiveness in joint visual feature extraction and nonlinear classifier learning. However, they require additional training data in the form of pixel-wise annotations, often manually drawn or delineated by tools with intensive user interaction. Such heavy annotation cost makes these methods less practical as pointed out in other applications.

In this dissertation, we address the aforementioned issue by proposing four methods for visual attention-getting object discovery. In the first work, we integrate the class-specific information into the visual attention-getting object discovery and then propose a weakly supervised learning method to reduce the annotation cost. The proposed method is composed of two CNN-based modules, image-level classifier and a pixel-level map generator with four losses. The results show that our approach outperforms the state-of-the-art weakly supervised methods and many fully supervised ones in both accuracy and efficiency. In the second work, we address unsupervised CNN-based object co-segmentation under an end-to-end trainable scheme and thus propose a co-attention CNNs to explore the inter-object consistency. The proposed method remarkably outperforms the state-of-the-art unsupervised and supervised methods on the standard object co-segmentation benchmarks. In the third work, we focus on unsupervised CNN-based object co-saliency detection and propose an end-to-end trainable graphical CNNs to jointly preserve the inter-object consistency and explore the intra-object saliency. The results show that our approach remarkably outperforms the state-of-the-art unsupervised methods and even surpasses many supervised DL-based saliency detection methods. In the final work, we tackle the CNN-based instance co-segmentation, which is a new and challenging task, and propose the concept, co-peak, to localize and segment each object instance in the given images. We develop a simple and effective method is developed for instance co-segmentation. The proposed method learns a model based on the {em fully convolutional network} (FCN) by optimizing three proposed losses. The learned model can reliably detect co-peaks and co-saliency maps for instance mask segmentation. Four datasets are collected for evaluating instance co-segmentation, and we achieve the state-of-the-art performance on these four datasets.
誌謝 iii
摘要 v
Abstract vii
1 Introduction 1
1.1 Thesis overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 A Category-Driven Map Generator for Single-image Saliency Detection
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Co-Attention CNNs for Object Co-Segmentation . . . . . . . . . 4
1.1.3 Graphical CNNs for Object Co-Saliency Detection . . . . . . . . 6
1.1.4 CNNs for Instance-Level Object Co-Segmentation . . . . . . . . 7
1.2 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 A Category-Driven Map Generator for Single-image Saliency Detection 11
2.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.1 Bottom-up object saliency detection . . . . . . . . . . . . . . . . 13
2.1.2 Top-down object saliency detection . . . . . . . . . . . . . . . . 14
2.1.3 CNN-based weakly supervised learning . . . . . . . . . . . . . . 16
2.1.4 Top-down neural attention . . . . . . . . . . . . . . . . . . . . . 18
2.2 Proposed approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.2 Our formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.3 Optimization process . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.4 Implementation details . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.1 Datasets and evaluation criterion . . . . . . . . . . . . . . . . . . 26
2.3.2 Results on the Graz-02 dataset . . . . . . . . . . . . . . . . . . . 27
2.3.3 Results on the PASCAL VOC-07 and VOC-12 datasets . . . . . . 34
2.3.4 Failure cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3 Co-Attention CNNs for Object Co-Segmentation 39
3.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.1.1 Object co-segmentation . . . . . . . . . . . . . . . . . . . . . . 40
3.1.2 Unsupervised CNN for image correspondence . . . . . . . . . . . 41
3.1.3 Weakly supervised semantic segmentation . . . . . . . . . . . . . 41
3.2 Proposed approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2.1 Proposed formulation . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2.2 Co-attention loss ℓc . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.3 Mask loss ℓm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.4 Optimization process . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2.5 Implementation details . . . . . . . . . . . . . . . . . . . . . . . 47
3.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3.1 Datasets and evaluation metrics . . . . . . . . . . . . . . . . . . 49
3.3.2 Comparison with co-segmentation methods . . . . . . . . . . . . 50
3.3.3 Comparison with WSS methods . . . . . . . . . . . . . . . . . . 53
4 Graphical CNNs for Object Co-Saliency Detection 55
4.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.1.1 Single-image saliency detection . . . . . . . . . . . . . . . . . . 56
4.1.2 Co-saliency detection . . . . . . . . . . . . . . . . . . . . . . . . 56
4.1.3 Graphical models with CNNs . . . . . . . . . . . . . . . . . . . 57
4.2 Proposed approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.1 Proposed formulation . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.2 Unary term s . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2.3 Pairwise term c . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.2.4 Co-saliency map enhancement . . . . . . . . . . . . . . . . . . . 62
4.2.5 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.2.6 Implementation details . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.3.1 Datasets and evaluation metrics . . . . . . . . . . . . . . . . . . 65
4.3.2 Comparison with state-of-the-art methods . . . . . . . . . . . . . 67
4.3.3 Ablation studies . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5 CNN-based Instance-Level Object Co-Segmentation 71
5.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.1.1 Object co-segmentation . . . . . . . . . . . . . . . . . . . . . . 72
5.1.2 Object co-localization . . . . . . . . . . . . . . . . . . . . . . . 73
5.1.3 Instance-aware segmentation . . . . . . . . . . . . . . . . . . . . 73
5.2 Proposed approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2.2 Co-peak search . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2.3 Instance mask segmentation . . . . . . . . . . . . . . . . . . . . 78
5.2.4 Implementation details . . . . . . . . . . . . . . . . . . . . . . . 79
5.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.3.1 Dataset collection . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.3.2 Evaluation metrics . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.3.3 Competing methods . . . . . . . . . . . . . . . . . . . . . . . . 82
5.3.4 Instance co-segmentation . . . . . . . . . . . . . . . . . . . . . . 84
5.3.5 Object co-localization . . . . . . . . . . . . . . . . . . . . . . . 86
6 Conclusion and Future Work 89
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
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