DeepStory: Video Story QA by Deep Embedded Memory Networks

Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos. We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data. The video stories are stored in a long-term memory component. For a given question, an LSTM-based attention model uses the long-term memory to recall the best question-story-answer triplet by focusing on specific words containing key information. We trained the DEMN on a novel QA dataset of children's cartoon video series, Pororo. The dataset contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained sentences for scene description, and 8,913 story-related QA pairs. Our experimental results show that the DEMN outperforms other QA models. This is mainly due to 1) the reconstruction of video stories in a scene-dialogue combined form that utilize the latent embedding and 2) attention. DEMN also achieved state-of-the-art results on the MovieQA benchmark.Comment: 7 pages, accepted for IJCAI 201

동적 멀티모달 데이터 학습을 위한 심층 하이퍼네트워크

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 장병탁.Recent advancements in information communication technology has led the explosive increase of data. Dissimilar to traditional data which are structured and unimodal, in particular, the characteristics of recent data generated from dynamic environments are summarized as high-dimensionality, multimodality, and structurelessness as well as huge-scale size. The learning from non-stationary multimodal data is essential for solving many difficult problems in artificial intelligence. However, despite many successful reports, existing machine learning methods have mainly focused on solving practical problems represented by large-scaled but static databases, such as image classification, tagging, and retrieval. Hypernetworks are a probabilistic graphical model representing empirical distribution, using a hypergraph structure that is a large collection of many hyperedges encoding the associations among variables. This representation allows the model to be suitable for characterizing the complex relationships between features with a population of building blocks. However, since a hypernetwork is represented by a huge combinatorial feature space, the model requires a large number of hyperedges for handling the multimodal large-scale data and thus faces the scalability problem. In this dissertation, we propose a deep architecture of hypernetworks for dealing with the scalability issue for learning from multimodal data with non-stationary properties such as videos, i.e., deep hypernetworks. Deep hypernetworks handle the issues through the abstraction at multiple levels using a hierarchy of multiple hypergraphs. We use a stochastic method based on Monte-Carlo simulation, a graph MC, for efficiently constructing hypergraphs representing the empirical distribution of the observed data. The structure of a deep hypernetwork continuously changes as the learning proceeds, and this flexibility is contrasted to other deep learning models. The proposed model incrementally learns from the data, thus handling the nonstationary properties such as concept drift. The abstract representations in the learned models play roles of multimodal knowledge on data, which are used for the content-aware crossmodal transformation including vision-language conversion. We view the vision-language conversion as a machine translation, and thus formulate the vision-language translation in terms of the statistical machine translation. Since the knowledge on the video stories are used for translation, we call this story-aware vision-language translation. We evaluate deep hypernetworks on large-scale vision-language multimodal data including benmarking datasets and cartoon video series. The experimental results show the deep hypernetworks effectively represent visual-linguistic information abstracted at multiple levels of the data contents as well as the associations between vision and language. We explain how the introduction of a hierarchy deals with the scalability and non-stationary properties. In addition, we present the story-aware vision-language translation on cartoon videos by generating scene images from sentences and descriptive subtitles from scene images. Furthermore, we discuss the meaning of our model for lifelong learning and the improvement direction for achieving human-level artificial intelligence.1 Introduction 1.1 Background and Motivation 1.2 Problems to be Addressed 1.3 The Proposed Approach and its Contribution 1.4 Organization of the Dissertation 2 RelatedWork 2.1 Multimodal Leanring 2.2 Models for Learning from Multimodal Data 2.2.1 Topic Model-Based Multimodal Leanring 2.2.2 Deep Network-based Multimodal Leanring 2.3 Higher-Order Graphical Models 2.3.1 Hypernetwork Models 2.3.2 Bayesian Evolutionary Learning of Hypernetworks 3 Multimodal Hypernetworks for Text-to-Image Retrievals 3.1 Overview 3.2 Hypernetworks for Multimodal Associations 3.2.1 Multimodal Hypernetworks 3.2.2 Incremental Learning of Multimodal Hypernetworks 3.3 Text-to-Image Crossmodal Inference 3.3.1 Representatation of Textual-Visual Data 3.3.2 Text-to-Image Query Expansion 3.4 Text-to-Image Retrieval via Multimodal Hypernetworks 3.4.1 Data and Experimental Settings 3.4.2 Text-to-Image Retrieval Performance 3.4.3 Incremental Learning for Text-to-Image Retrieval 3.5 Summary 4 Deep Hypernetworks for Multimodal Cocnept Learning from Cartoon Videos 4.1 Overview 4.2 Visual-Linguistic Concept Representation of Catoon Videos 4.3 Deep Hypernetworks for Modeling Visual-Linguistic Concepts 4.3.1 Sparse Population Coding 4.3.2 Deep Hypernetworks for Concept Hierarchies 4.3.3 Implication of Deep Hypernetworks on Cognitive Modeling 4.4 Learning of Deep Hypernetworks 4.4.1 Problem Space of Deep Hypernetworks 4.4.2 Graph Monte-Carlo Simulation 4.4.3 Learning of Concept Layers 4.4.4 Incremental Concept Construction 4.5 Incremental Concept Construction from Catoon Videos 4.5.1 Data Description and Parameter Setup 4.5.2 Concept Representation and Development 4.5.3 Character Classification via Concept Learning 4.5.4 Vision-Language Conversion via Concept Learning 4.6 Summary 5 Story-awareVision-LanguageTranslation usingDeepConcept Hiearachies 5.1 Overview 5.2 Vision-Language Conversion as a Machine Translation 5.2.1 Statistical Machine Translation 5.2.2 Vision-Language Translation 5.3 Story-aware Vision-Language Translation using Deep Concept Hierarchies 5.3.1 Story-aware Vision-Language Translation 5.3.2 Vision-to-Language Translation 5.3.3 Language-to-Vision Translation 5.4 Story-aware Vision-Language Translation on Catoon Videos 5.4.1 Data and Experimental Setting 5.4.2 Scene-to-Sentence Generation 5.4.3 Sentence-to-Scene Generation 5.4.4 Visual-Linguistic Story Summarization of Cartoon Videos 5.5 Summary 6 Concluding Remarks 6.1 Summary of the Dissertation 6.2 Directions for Further Research Bibliography 한글초록Docto

Developing Video Based Language Learning to Support Students’ Autonomous Study in Higher Education Setting

The pilot study focuses on three issues: how to articulate the need for learning videos in the language course, how to design learning videos in the language course, and how to articulate the feasibility and practicality of learning videos in the language course at the Faculty of Letters, Muslim University of Indonesia. This study employs a qualitative descriptive approach. The findings of this study indicated that students required more engaging learning resources than books and presentation slides, one of which is a learning video packaged in animation to capture students' attention and motivate them to learn the content or material in the learning video. Language learning videos with material paragraphs are produced using the video animation concept, specifically whiteboard animation. This design was picked based on the animated video's attractiveness to the audience in order to pique their interest in the material or film after it is shown. The viability of learning videos as determined by validation results from media and material experts on newly designed Indonesian language learning films is extremely good. The video's form or presentation, as well as the content included inside, are adequate and sufficient to allow it to forward to the following stage, namely testing to ascertain the answers or replies of lecturers and students. The practicality of the learning videos, as determined by lecturers and students' reactions, is extremely good; there are no substantial barriers to lecturers and students using these learning videos

Discourses, Modes, Media and Meaning in an Era of Pandemic

The COVID-19 pandemic has affected all aspects of our everyday lives – from the political to the economic to the social. Using a multimodal discourse analysis approach, this dynamic collection examines various discourses, modes and media in circulation during the early stages of the pandemic, and how these have impacted our daily lives in terms of the various meanings they express. Examples include how national and international news organisations communicate important information about the virus and the crisis, the public’s reactions to such communications, the resultant (counter-)discourses as manifested in social media posts and memes, as well as the impact social distancing policies and mobility restrictions have had on people’s communication and interaction practices. The book offers a synoptic view of how the pandemic was communicated, represented and (re-)contextualised across different spheres, and ultimately hopes to help account for the significant changes we are continuing to witness in our everyday lives as the pandemic unfolds. This volume will appeal primarily to scholars in the field of (multimodal) discourse analysis. It will also be of interest to researchers and graduate students in other fields whose work focuses on the use of multimodal artefacts for communication and meaning making

Discourses, Modes, Media and Meaning in an Era of Pandemic

The COVID-19 pandemic has affected all aspects of our everyday lives – from the political to the economic to the social. Using a multimodal discourse analysis approach, this dynamic collection examines various discourses, modes and media in circulation during the early stages of the pandemic, and how these have impacted our daily lives in terms of the various meanings they express. Examples include how national and international news organisations communicate important information about the virus and the crisis, the public’s reactions to such communications, the resultant (counter-)discourses as manifested in social media posts and memes, as well as the impact social distancing policies and mobility restrictions have had on people’s communication and interaction practices. The book offers a synoptic view of how the pandemic was communicated, represented and (re-)contextualised across different spheres, and ultimately hopes to help account for the significant changes we are continuing to witness in our everyday lives as the pandemic unfolds. This volume will appeal primarily to scholars in the field of (multimodal) discourse analysis. It will also be of interest to researchers and graduate students in other fields whose work focuses on the use of multimodal artefacts for communication and meaning making

Text-to-picture tools, systems, and approaches: a survey

Text-to-picture systems attempt to facilitate high-level, user-friendly communication between humans and computers while promoting understanding of natural language. These systems interpret a natural language text and transform it into a visual format as pictures or images that are either static or dynamic. In this paper, we aim to identify current difficulties and the main problems faced by prior systems, and in particular, we seek to investigate the feasibility of automatic visualization of Arabic story text through multimedia. Hence, we analyzed a number of well-known text-to-picture systems, tools, and approaches. We showed their constituent steps, such as knowledge extraction, mapping, and image layout, as well as their performance and limitations. We also compared these systems based on a set of criteria, mainly natural language processing, natural language understanding, and input/output modalities. Our survey showed that currently emerging techniques in natural language processing tools and computer vision have made promising advances in analyzing general text and understanding images and videos. Furthermore, important remarks and findings have been deduced from these prior works, which would help in developing an effective text-to-picture system for learning and educational purposes. - 2019, The Author(s).This work was made possible by NPRP grant #10-0205-170346 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

잠재 임베딩을 통한 시각적 스토리로부터의 서사 텍스트 생성기 학습

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 장병탁.The ability to understand the story is essential to make humans unique from other primates as well as animals. The capability of story understanding is crucial for AI agents to live with people in everyday life and understand their context. However, most research on story AI focuses on automated story generation based on closed worlds designed manually, which are widely used for computation authoring. Machine learning techniques on story corpora face similar problems of natural language processing such as omitting details and commonsense knowledge. Since the remarkable success of deep learning on computer vision field, increasing our interest in research on bridging between vision and language, vision-grounded story data will potentially improve the performance of story understanding and narrative text generation. Let us assume that AI agents lie in the environment in which the sensing information is input by the camera. Those agents observe the surroundings, translate them into the story in natural language, and predict the following event or multiple ones sequentially. This dissertation study on the related problems: learning stories or generating the narrative text from image streams or videos. The first problem is to generate a narrative text from a sequence of ordered images. As a solution, we introduce a GLAC Net (Global-local Attention Cascading Network). It translates from image sequences to narrative paragraphs in text as a encoder-decoder framework with sequence-to-sequence setting. It has convolutional neural networks for extracting information from images, and recurrent neural networks for text generation. We introduce visual cue encoders with stacked bidirectional LSTMs, and all of the outputs of each layer are aggregated as contextualized image vectors to extract visual clues. The coherency of the generated text is further improved by conveying (cascading) the information of the previous sentence to the next sentence serially in the decoders. We evaluate the performance of it on the Visual storytelling (VIST) dataset. It outperforms other state-of-the-art results and shows the best scores in total score and all of 6 aspects in the visual storytelling challenge with evaluation of human judges. The second is to predict the following events or narrative texts with the former parts of stories. It should be possible to predict at any step with an arbitrary length. We propose recurrent event retrieval models as a solution. They train a context accumulation function and two embedding functions, where make close the distance between the cumulative context at current time and the next probable events on a latent space. They update the cumulative context with a new event as a input using bilinear operations, and we can find the next event candidates with the updated cumulative context. We evaluate them for Story Cloze Test, they show competitive performance and the best in open-ended generation setting. Also, it demonstrates the working examples in an interactive setting. The third deals with the study on composite representation learning for semantics and order for video stories. We embed each episode as a trajectory-like sequence of events on the latent space, and propose a ViStoryNet to regenerate video stories with them (tasks of story completion). We convert event sentences to thought vectors, and train functions to make successive event embed close each other to form episodes as trajectories. Bi-directional LSTMs are trained as sequence models, and decoders to generate event sentences with GRUs. We test them experimentally with PororoQA dataset, and observe that most of episodes show the form of trajectories. We use them to complete the blocked part of stories, and they show not perfect but overall similar result. Those results above can be applied to AI agents in the living area sensing with their cameras, explain the situation as stories, infer some unobserved parts, and predict the future story.스토리를 이해하는 능력은 동물들 뿐만 아니라 다른 유인원과 인류를 구별짓는 중요한 능력이다. 인공지능이 일상생활 속에서 사람들과 함께 지내면서 그들의 생활 속 맥락을 이해하기 위해서는 스토리를 이해하는 능력이 매우 중요하다. 하지만, 기존의 스토리에 관한 연구는 언어처리의 어려움으로 인해 사전에 정의된 세계 모델 하에서 좋은 품질의 저작물을 생성하려는 기술이 주로 연구되어 왔다. 기계학습 기법을 통해 스토리를 다루려는 시도들은 대체로 자연어로 표현된 데이터에 기반할 수 밖에 없어 자연어 처리에서 겪는 문제들을 동일하게 겪는다. 이를 극복하기 위해서는 시각적 정보가 함께 연동된 데이터가 도움이 될 수 있다. 최근 딥러닝의 눈부신 발전에 힘입어 시각과 언어 사이의 관계를 다루는 연구들이 늘어나고 있다. 연구의 비전으로서, 인공지능 에이전트가 주변 정보를 카메라로 입력받는 환경 속에 놓여있는 상황을 생각해 볼 수 있다. 이 안에서 인공지능 에이전트는 주변을 관찰하면서 그에 대한 스토리를 자연어 형태로 생성하고, 생성된 스토리를 바탕으로 다음에 일어날 스토리를 한 단계에서 여러 단계까지 예측할 수 있다. 본 학위 논문에서는 사진 및 비디오 속에 나타나는 스토리(visual story)를 학습하는 방법, 내러티브 텍스트로의 변환, 가려진 사건 및 다음 사건을 추론하는 연구들을 다룬다. 첫 번째로, 여러 장의 사진이 주어졌을 때 이를 바탕으로 스토리 텍스트를 생성하는 문제(비주얼 스토리텔링)를 다룬다. 이 문제 해결을 위해 글랙넷(GLAC Net)을 제안하였다. 먼저, 사진들로부터 정보를 추출하기 위한 컨볼루션 신경망, 문장을 생성하기 위해 순환신경망을 이용한다. 시퀀스-시퀀스 구조의 인코더로서, 전체적인 이야기 구조의 표현을 위해 다계층 양방향 순환신경망을 배치하되 각 사진 별 정보를 함께 이용하기 위해 전역적-국부적 주의집중 모델을 제안하였다. 또한, 여러 문장을 생성하는 동안 맥락정보와 국부정보를 잃지 않게 하기 위해 앞선 문장 정보를 전달하는 메커니즘을 제안하였다. 위 제안 방법으로 비스트(VIST) 데이터 집합을 학습하였고, 제 1 회 시각적 스토리텔링 대회(visual storytelling challenge)에서 사람 평가를 기준으로 전체 점수 및 6 항목 별로 모두 최고점을 받았다. 두 번째로, 스토리의 일부가 문장들로 주어졌을 때 이를 바탕으로 다음 문장을 예측하는 문제를 다룬다. 임의의 길이의 스토리에 대해 임의의 위치에서 예측이 가능해야 하고, 예측하려는 단계 수에 무관하게 작동해야 한다. 이를 위한 방법으로 순환 사건 인출 모델(Recurrent Event Retrieval Models)을 제안하였다. 이 방법은 은닉 공간 상에서 현재까지 누적된 맥락과 다음에 발생할 유력 사건 사이의 거리를 가깝게 하도록 맥락누적함수와 두 개의 임베딩 함수를 학습한다. 이를 통해 이미 입력되어 있던 스토리에 새로운 사건이 입력되면 쌍선형적 연산을 통해 기존의 맥락을 개선하여 다음에 발생할 유력한 사건들을 찾는다. 이 방법으로 락스토리(ROCStories) 데이터집합을 학습하였고, 스토리 클로즈 테스트(Story Cloze Test)를 통해 평가한 결과 경쟁력 있는 성능을 보였으며, 특히 임의의 길이로 추론할 수 있는 기법 중에 최고성능을 보였다. 세 번째로, 비디오 스토리에서 사건 시퀀스 중 일부가 가려졌을 때 이를 복구하는 문제를 다룬다. 특히, 각 사건의 의미 정보와 순서를 모델의 표현 학습에 반영하고자 하였다. 이를 위해 은닉 공간 상에 각 에피소드들을 궤적 형태로 임베딩하고, 이를 바탕으로 스토리를 재생성을 하여 스토리 완성을 할 수 있는 모델인 비스토리넷(ViStoryNet)을 제안하였다. 각 에피소드를 궤적 형태를 가지게 하기 위해 사건 문장을 사고벡터(thought vector)로 변환하고, 연속 이벤트 순서 임베딩을 통해 전후 사건들이 서로 가깝게 임베딩되도록 하여 하나의 에피소드가 궤적의 모양을 가지도록 학습하였다. 뽀로로QA 데이터집합을 통해 실험적으로 결과를 확인하였다. 임베딩 된 에피소드들은 궤적 형태로 잘 나타났으며, 에피소드들을 재생성 해본 결과 전체적인 측면에서 유사한 결과를 보였다. 위 결과물들은 카메라로 입력되는 주변 정보를 바탕으로 스토리를 이해하고 일부 관측되지 않은 부분을 추론하며, 향후 스토리를 예측하는 방법들에 대응된다.Abstract i Chapter 1 Introduction 1 1.1 Story of Everyday lives in Videos and Story Understanding . . . 1 1.2 Problems to be addressed . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Approach and Contribution . . . . . . . . . . . . . . . . . . . . . 6 1.4 Organization of Dissertation . . . . . . . . . . . . . . . . . . . . . 9 Chapter 2 Background and Related Work 10 2.1 Why We Study Stories . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Latent Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Order Embedding and Ordinal Embedding . . . . . . . . . . . . 14 2.4 Comparison to Story Understanding . . . . . . . . . . . . . . . . 15 2.5 Story Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.5.1 Abstract Event Representations . . . . . . . . . . . . . . . 17 2.5.2 Seq-to-seq Attentional Models . . . . . . . . . . . . . . . . 18 2.5.3 Story Generation from Images . . . . . . . . . . . . . . . 19 Chapter 3 Visual Storytelling via Global-local Attention Cascading Networks 21 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Evaluation for Visual Storytelling . . . . . . . . . . . . . . . . . . 26 3.3 Global-local Attention Cascading Networks (GLAC Net) . . . . . 27 3.3.1 Encoder: Contextualized Image Vector Extractor . . . . . 28 3.3.2 Decoder: Story Generator with Attention and Cascading Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4.1 VIST Dataset . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4.2 Experiment Settings . . . . . . . . . . . . . . . . . . . . . 33 3.4.3 Network Training Details . . . . . . . . . . . . . . . . . . 36 3.4.4 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . 38 3.4.5 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . 38 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Chapter 4 Common Space Learning on Cumulative Contexts and the Next Events: Recurrent Event Retrieval Models 44 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2 Problems of Context Accumulation . . . . . . . . . . . . . . . . . 45 4.3 Recurrent Event Retrieval Models for Next Event Prediction . . 46 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4.2 Story Cloze Test . . . . . . . . . . . . . . . . . . . . . . . 52 4.4.3 Open-ended Story Generation . . . . . . . . . . . . . . . . 53 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Chapter 5 ViStoryNet: Order Embedding of Successive Events and the Networks for Story Regeneration 58 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.2 Order Embedding with Triple Learning . . . . . . . . . . . . . . 60 5.2.1 Embedding Ordered Objects in Sequences . . . . . . . . . 62 5.3 Problems and Contextual Events . . . . . . . . . . . . . . . . . . 62 5.3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . 62 5.3.2 Contextual Event Vectors from Kids Videos . . . . . . . . 64 5.4 Architectures for the Story Regeneration Task . . . . . . . . . . . 67 5.4.1 Two Sentence Generators as Decoders . . . . . . . . . . . 68 5.4.2 Successive Event Order Embedding (SEOE) . . . . . . . . 68 5.4.3 Sequence Models of the Event Space . . . . . . . . . . . . 72 5.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.5.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . 73 5.5.2 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . 73 5.5.3 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . 74 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Chapter 6 Concluding Remarks 80 6.1 Summary of Methods and Contributions . . . . . . . . . . . . . . 80 6.2 Limitation and Outlook . . . . . . . . . . . . . . . . . . . . . . . 81 6.3 Suggestions for Future Research . . . . . . . . . . . . . . . . . . . 81 초록 101Docto