A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions

LINEAR UNMIXING PROTOCOL FOR HYPERSPECTRAL IMAGE FUSION ANALYSIS APPLIED TO A CASE STUDY OF VEGETAL TISSUES

Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms

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

Recognizing Human-Object Interactions in Videos

Understanding human actions that involve interacting with objects is very important due to the wide range of real-world applications, such as security surveillance and healthcare. In this thesis, three different approaches are presented for addressing the problem of human-object interactions (HOIs) recognition in videos. Firstly, we propose a hierarchical framework for analyzing human-object interactions in a video sequence. The framework comprises Long Short-Term Memory (LSTM) networks that capture human motion and temporal object information independently. These pieces of information are then combined through a bilinear layer and fed into a global deep LSTM to learn high-level information about HOIs. To concentrate on the key components of human and object temporal information, the proposed approach incorporates an attention mechanism into LSTMs. Secondly, we aim to achieve a holistic understanding of human-object interactions (HOIs) by exploiting both their local and global contexts through knowledge distillation. The local context graphs are used to learn the relationship between humans and objects at the frame level by capturing their co-occurrence at a specific time step. On the other hand, the global relation graph is constructed based on the video-level of human and object interactions, identifying their long-term relations throughout a video sequence. We investigate how knowledge from these context graphs can be distilled to their counterparts to improve HOI recognition. Lastly, we propose the Spatio-Temporal Interaction Transformer-based (STIT) network to reason about spatio-temporal changes of humans and objects. Specifically, the spatial transformers learn the local context of humans and objects at specific frame times. The temporal transformer then learns the relations at a higher level between spatial context representations at different time steps, capturing long-term dependencies across frames. We further investigate multiple hierarchy designs for learning human interactions. The effectiveness of each of the proposed methods mentioned above is evaluated using various video action datasets that include human-object interactions, such as Charades, CAD-120, and Something-Something V1