利用多聲學專家模型改進多模態電影場景分割

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研究生：	林孟翰 Lin, Meng-Han
論文名稱：	利用多聲學專家模型改進多模態電影場景分割 Improving Multimodal Movie Scene Segmentation Using Mixture of Acoustic Experts
指導教授：	李祈均 Lee, Chi-Chun
口試委員:	林彥宇 Lin, Yen-Yu 陳奕廷 Chen, Yi-Ting
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2022
畢業學年度：	110
語文別：	英文
論文頁數：	38
中文關鍵詞：	電影、場景分割、多專家模型、多模態注意力機制、聲訊
外文關鍵詞：	Movie, Scene Segmentation, Mixture of Experts, Multimodal Attention, Audio
相關次數：	點閱：2 下載：0
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近年來大量增加的多媒體影音內容促進了各種多媒體技術的發展，而這些多媒體技術都有把長的影片分割成較短且有意義的片段作為前置處理的需求。在一部電影中，場景(scene)是最基本的語義單位，而場景分割則是各種多媒體技術中關鍵的前置處理。先前場景分割的研究從幀(frame)或鏡頭(shot)中提取低階的視覺特徵來做分群，並且對分群施加時間上的限制，使得分群的結果能符合場景時序上的特性。最近的研究則進一步以多模態語義特徵訓練能辨識場景的模型。在多模態語義特徵中，音訊特徵通常是以在多領域皆適用的預訓練模型提取，並沒有考慮到電影音訊中具有不同語義的各種面向。除此之外，用於場景分割的音訊和影像互補的特性和複雜的互動也經常被忽略。在此篇研究中，我們提出能夠整合聲學模型和多模態模型的多聲學專家模型（MOAE）來提升場景分割的品質。聲學模型是針對音訊中各種語義面向訓練而成，其中包括講者、環境音等其他音訊事件的特徵都被當作是聲學模型的訓練資料。MOAE 能夠優化各個專家模型的權重，並在具有 1,110 個場景邊界 (scene boundary) 的電影資料庫中達到 61.89% F1-score。我們將MOAE分配給各專家模型的權重視覺化，並以圖像說明專家模型之間的互補的特性是如何改進場景分割的結果。

The recent growth of multimedia content brought about the accelerated development of multimedia computing technology. Such technology requires the segmentation of long videos, such as movies, into semantically meaningful clips as pre-processing. Scenes are the basic semantic elements in a movie. Past studies proposed clustering shots into scenes based on low-level visual representations with additional temporal constraints and alignment mechanisms. Recently, researchers proposed the use of multimodal semantic features to represent the complex semantic aspects of a movie. However, the semantically meaningful aspects of audio are often ignored because acoustic representations are usually extracted using a universally pretrained model. Moreover, the discussion and further research into the interactions between the aspects of audio and visual modalities were left much to be desired. In this work, we introduce a mixture-of-audio-experts (MOAE) framework. It has the ability to integrate acoustic and multimodal expert models that are learned based on different acoustic semantics, including speaker, environmental sounds along with visual representations. The mixer network in the MOAE determines the importance of different modalities and aspects by optimizing the weights assigned to each expert model. Our framework achieves a state-of-the-art 61.89% F1-score for scene segmentation on a database with 1,110 scenes. We also provide the visualization of the assigned weights along with corresponding sampled images/clips. These visualizations reveal the complementary properties among visual and acoustic modalities that lead to the improvement of scene segmentation.

摘要    ---------------------------------------------------------------------------i
Abstract-----------------------------------------------------------------------ii
誌謝---------------------------------------------------------------------------iii
Contents-----------------------------------------------------------------------iv
List of Figures----------------------------------------------------------------vi
List of Tables-----------------------------------------------------------------viii
Chapter 1 Introduction---------------------------------------------------------1
1.1 Background-----------------------------------------------------------------1
1.2 Motivation and Proposal----------------------------------------------------3
Chapter 2 Database and Methodology---------------------------------------------5
2.1 Database and Scene Annotation----------------------------------------------5
2.2 Multimodal Shot Representations--------------------------------------------8
2.2.1 Visual Shot Representations----------------------------------------------8
2.2.2 Acoustic Shot Representations--------------------------------------------9
2.2.3 Feature Encoding---------------------------------------------------------9
2.3 Framework------------------------------------------------------------------10
2.3.1 Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM)----11
2.3.2 Acoustic Expert and Segment Prediction-----------------------------------12
2.3.3 Multimodal Expert and Segment Prediction---------------------------------13
2.3.4 Mixer Network and Constraints--------------------------------------------15
Chapter 3 Experiment-----------------------------------------------------------19
3.1 Experimental Setup---------------------------------------------------------19
3.1.1 Network Configurations---------------------------------------------------20
Chapter 4 Results and Analysis-------------------------------------------------21
4.1 Comparison of Unimodal and Multimodal Experts------------------------------22
4.2 Comparison of Ensemble Approaches------------------------------------------24
4.3 Weight Analysis------------------------------------------------------------24
Chapter 5 Discussions----------------------------------------------------------27
Chapter 6 Conclusions----------------------------------------------------------29
Reference----------------------------------------------------------------------30
Appendix-----------------------------------------------------------------------34
Notations in Demo Clips--------------------------------------------------------34
Clips Analysis-----------------------------------------------------------------34
                                

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