Pair-Wise Temporal Pooling Method for Rapid Training of the HTM Networks Used in Computer Vision Applications

In the paper, several modifications to the conventional learning algorithms of the Hierarchical Temporal Memory (HTM) -- a biologically inspired large-scale model of the neocortex by Numenta -- have been proposed. Firstly, an alternative spatial pooling method has been introduced, which makes use of a random pattern generator exploiting the Metropolis-Hastings algorithm. The original inference algorithm by Numenta has been reformulated, in order to reduce a number of tunable parameters and to optimize its computational efficiency. The main contribution of the paper consists in the proposal of a novel temporal pooling method -- the pair-wise explorer -- which allows faster and more reliable training of the HTM networks using data without inherent temporal information (e.g., static images). While the conventional temporal pooler trains the HTM network on a finite segment of the smooth Brownian-like random walk across the training images, the proposed method performs training by means of the pairs of patterns randomly sampled (in a special manner) from a virtually infinite smooth random walk. We have conducted a set of experiments with the single-layer HTM network applied to the position, scale, and rotation-invariant recognition of geometric objects. The obtained results provide a clear evidence that the pair-wise method yields significantly faster convergence to the theoretical maximum of the classification accuracy with respect to both the length of the training sequence (defined by the maximum allowed number of updates of the time adjacency matrix -- TAM) and the number of training patterns. The advantage of the proposed explorer manifested itself mostly in the lower range of TAM updates where it caused up to 10 % relative accuracy improvement over the conventional method. Therefore we suggest to use the pair-wise explorer, instead of the smooth explorer, always when the HTM network is trained on a set of static images, especially when the exhaustive training is impossible due to the complexity of the given task

Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges

Facial affect analysis (FAA) using visual signals is important in human-computer interaction. Early methods focus on extracting appearance and geometry features associated with human affects, while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. In this paper, we provide a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches that are widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their usage of traditional methods or deep models, with a special emphasis on the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.Comment: 20 pages, 12 figures, 5 table

이야기형 설명문을 활용한 대규모 비디오 학습 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 김건희.Extensive contributions are being made to develop intelligent agents that can recognize and communicate with the world. In this sense, various video-language tasks have drawn a lot of interests in computer vision research, including image/video captioning, video retrieval and video question answering. It can be applied to high-level computer vision tasks and various future industries such as search engines, social marketing, automated driving, and robotics support through QA / dialog generation for the surrounding environment. However, despite these developments, video-language learning suffers from a higher degree of complexity. This thesis investigates methodologies for learning the relationship between videos and free-formed languages, including explanations, conversations, and question-and-answers, so that the machine can easily adapt to target downstream tasks. First, we introduce several methods to learn the relationship between long sentences and videos efficiently. We introduce the approaches for supervising human attention transfer for the video attention model, which shows the video attention mechanism can benefit from explicit human gaze labels. Next, we introduce the end-to-end semantic attention method, which further reduces the visual attention algorithm's complexity by using the representative visual concept word detected by the attention-based detector. As a follow-up study on previous methods, we introduce a JSFusion (Joint Sequence Fusion) method that enables efficient video search and QA by enabling many-to-many matching of attention model. Next, we introduce the CiSIN(Character in Story Identification Network), which uses Attention to increase the performance of character grounding and character re-identification in the movie. Finally, we introduce Transitional Adaptation, which promotes the caption generation models to generates coherent narratives for long videos. In summary, this thesis presents a novel approaches for automatic video description generation/retrieval and shows the benefits of extracting linguistic knowledge for object and motion in the video as well as the advantage of multimodal audio-visual learning for understanding videos. Since the proposed methods are easily adapted to any video-language tasks, it is expected to be applied to the latest models, bringing additional performance improvements. Moving forward, we plan to design an unsupervised video learning framework that can solve many challenges in the industry by integrating an unlimited amount of video, audio, and free-formed language data from the web.시각-언어 학습은 이미지/비디오 캡션(Image/Video captioning), 시각 질의응답(Visual Question and Answering), 비디오 검색(Video Retrieval), 장면 이해(scene understanding), 이벤트 인식(event detection) 등 고차원의 컴퓨터 비전 태스크(task)뿐만 아니라 주변 환경에 대한 질의 응답 및 대화 생성(Dialogue Generation)으로 인터넷 검색 뿐만 아니라 최근 활발한 소셜 마케팅(Social Marketing) 자율 주행(Automated Driving), 로보틱스(Robotics)을 보조하는 등 여러 미래 산업에 적용될 수 있어 활발히 연구되고 있는 중요한 분야이다. 컴퓨터 비젼과 자연어 처리는 이러한 중요성을 바탕으로 각자 고유한 영역에서 발전을 거듭해 왔으나, 최근 딥러닝의 등장과 함께 눈부시게 발전하면서 서로를 보완하며 학습 결과를 향상시키는 등 큰 시너지 효과를 발휘하게 되었다. 하지만 이런 발전에도 불구하고, 비디오-언어간 학습은 문제의 복잡도가 한층 높아 어려움을 겪게 되는 경우가 많다. 본 논문에서는 비디오와 이에 대응하는 설명, 대화, 질의 응답 등 더 나아가 자유 형태의 언어 (Free-formed language)간의 관계를 더욱 효율적으로 학습하고, 목표 임무에 잘 대응할 수 있도록 개선하는 것을 목표로 한다. 먼저, 시각적 복잡도가 이미지보다 높은 비디오와 긴 문장 사이의 관계를 효율적으로 학습하기 위한 여러 방법들을 소개한다. 인간의 주의 인식(Attention) 모델을 비디오-언어 모델에 지도 학습 하는 방법을 소개하고, 이어서 비디오에서 우선 검출된 대표 시각 단어를 매개로 하여 주의 인식(Attention) 알고리즘의 복잡도를 더욱 줄이는 의미 중심 주의 인식 (Semantic Attention) 방법, 어텐션 모델의 다대다 매칭을 기반으로 효율적인 비디오 검색 및 질의응답을 가능케 하는 비디오-언어간 융합 (Joint Sequence Fusion) 방법 등 비디오 주의 인식을 효율적으로 학습시킬 수 있는 방법들을 제시한다. 다음으로는, 주의 인식(Attention) 모델이 물체-단어 간 관계를 넘어 비디오 상에서 인물 검색 (Person Searching) 그리고 인물 재 식별 (Person Re-Identification)을 동시에 수행하며 상승작용을 일으키는 스토리 속 캐릭터 인식 신경망 (Character in Story Identification Network) 을 소개하며, 마지막으로 자기 지도 학습(Self-supervised Learning)을 통해 주의 인식(Attention) 기반 언어 모델이 긴 비디오에 대한 설명을 연관성 있게 잘 생성할 수 있도록 유도하는 방법을 소개한다. 요약하자면, 이 학위 논문에서 제안한 새로운 방법론들은 비디오-언어 학습에 해당하는 비디오 캡션(Video captioning), 비디오 검색(Video Retrieval), 시각 질의응답(Video Question and Answering)등을 해결할 수 있는 기술적 디딤돌이 되며, 비디오 캡션 학습을 통해 학습된 주의 인식 모듈은 검색 및 질의응답, 인물 검색 등 각 네트워크에 이식되면서 새로운 문제들에 대해 동시에 최고 수준(State-of-the-art)의 성능을 달성하였다. 이를 통해 비디오-언어 학습으로 얻은 언어 지식의 이전은 시각-청각을 아우르는 비디오 멀티모달 학습에 큰 도움이 되는 것을 실험적으로 보여준다. 향후 작업 방향 (Future Work)으로는 앞서 연구한 내용들을 기반으로 웹 속에 존재하는 대규모의 언어, 비디오, 오디오 데이터를 통합해 학습에 활용하여 산업계의 많은 난제를 해결할 수 있는 비지도 학습 모델을 만들고자 한다.Chapter 1 Introduction 1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 1.2 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . .8 Chapter 2 Related Work 2.1 Video Captioning . . . . . . . . . . . . . . . . . . . . . . . . . . .9 2.2 Video Retrieval with Natural Language . . . . . . . . . . . . . . 12 2.3 Video Question and Answering . . . . . . . . . . . . . . . . . . . 13 2.4 Cross-modal Representation Learning for Vision and LanguageTasks . . . . 15 Chapter 3 Human Attention Transfer for Video Captioning18 3.1 Introduction 3.2 Video Datasets for Caption and Gaze . . . . . . . . . . . . . . . 21 3.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.1 Video Pre-processing and Description . . . . . . . . . . . 22 3.3.2The Recurrent Gaze Prediction (RGP) Model . . . . . . . 23 3.3.3Construction of Visual Feature Pools . . . . . . . . . . . . 24 3.3.4The Decoder for Caption Generation . . . . . . . . . . . . 26 3.3.5Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4.1Evaluation of Gaze Prediction . . . . . . . . . . . . . . . . 29 3.4.2Evaluation of Video Captioning . . . . . . . . . . . . . . . 32 3.4.3Human Evaluation via AMT . . . . . . . . . . . . . . . . 35 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Chapter 4 Semantic Word Attention for Video QA and VideoCaptioning 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.1Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.1.2Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.1Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.2An Attention Model for Concept Detection . . . . . . . . 42 4.2.3Video-to-Language Models . . . . . . . . . . . . . . . . . 45 4.2.4A Model for Description . . . . . . . . . . . . . . . . . . . 45 4.2.5A Model for Fill-in-the-Blank . . . . . . . . . . . . . . . . 48 4.2.6A Model for Multiple-Choice Test . . . . . . . . . . . . . 50 4.2.7A Model for Retrieval . . . . . . . . . . . . . . . . . . . . 51 4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.1The LSMDC Dataset and Tasks . . . . . . . . . . . . . . 52 4.3.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 54 4.3.3Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 56 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Chapter 5 Joint Sequnece Fusion Attention for Multimodal Sequence Data 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.3.1Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.3.2The Joint Semantic Tensor . . . . . . . . . . . . . . . . . 65 5.3.3The Convolutional Hierarchical Decoder . . . . . . . . . . 66 5.3.4An Illustrative Example of How the JSFusion Model Works 68 5.3.5Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.3.6Implementation of Video-Language Models . . . . . . . . 69 5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.4.1LSMDC Dataset and Tasks . . . . . . . . . . . . . . . . . 71 5.4.2MSR-VTT-(RET/MC) Dataset and Tasks . . . . . . . . . 73 5.4.3Quantitative Results . . . . . . . . . . . . . . . . . . . . . 74 5.4.4Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 76 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Chapter 6 Character Re-Identification and Character Ground-ing for Movie Understanding 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 6.3.1Video Preprocessing . . . . . . . . . . . . . . . . . . . . . 84 6.3.2Visual Track Embedding . . . . . . . . . . . . . . . . . . . 85 6.3.3Textual Character Embedding . . . . . . . . . . . . . . . 86 6.3.4Character Grounding . . . . . . . . . . . . . . . . . . . . 87 6.3.5Re-Identification . . . . . . . . . . . . . . . . . . . . . . . 88 6.3.6Joint Training . . . . . . . . . . . . . . . . . . . . . . . . 90 6.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.4.1Experimental Setup . . . . . . . . . . . . . . . . . . . . . 92 6.4.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 93 6.4.3Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 95 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Chapter 7 Transitional Adaptation of Pretrained Models forVisual Storytelling 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 7.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.3.1The Visual Encoder . . . . . . . . . . . . . . . . . . . . . 104 7.3.2The Language Generator . . . . . . . . . . . . . . . . . . 104 7.3.3Adaptation training . . . . . . . . . . . . . . . . . . . . . 105 7.3.4The Sequential Coherence Loss . . . . . . . . . . . . . . . 105 7.3.5Training with the adaptation Loss . . . . . . . . . . . . . 107 7.3.6Fine-tuning and Inference . . . . . . . . . . . . . . . . . . 107 7.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 7.4.1Experimental Setup . . . . . . . . . . . . . . . . . . . . . 109 7.4.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 112 7.4.3Further Analyses . . . . . . . . . . . . . . . . . . . . . . . 112 7.4.4Human Evaluation Results . . . . . . . . . . . . . . . . . 115 7.4.5Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 116 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Chapter 8 Conclusion 8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 8.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Bibliography ... 123 요약 ... 148 Acknowledgements ... 150Docto

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Autonomous vehicles are becoming central for the future of mobility, supported by advances in deep learning techniques. The performance of aself-driving system is highly dependent on the quality of the perception task. Developments in sensor technologies have led to an increased availability of 3D scanners such as LiDAR, allowing for a more accurate representation of the vehicle's surroundings, leading to safer systems. The rapid development and consequent rise of research studies around self-driving systems since early 2010, resulted in a tremendous increase in the number and novelty of object detection methods. After the first wave of works that essentially tried to expand known techniques from object detection in images, more recently there has been a notable development in newer and more adapted to LiDAR data works. This paper addresses the existing literature on object detection using LiDAR data within the scope of self-driving and brings a systematic way for analysing it. Unlike general object detection surveys, we will focus on point-cloud data, which presents specific challenges, notably its high-dimensional and sparse nature. This work introduces a common object detection pipeline and taxonomy to facilitate a thorough comparison between different techniques and, departing from it, this work will critically examine the representation of data (critical for complexity reduction), feature extraction and finally the object detection models. A comparison between performance results of the different models is included, alongside with some future research challenges.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n. 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Deep Learning For Sequential Pattern Recognition

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM)In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with the conventional algorithms for optimization and training make them efficient for variety of applications in signal processing and pattern recognition. This thesis explores these novel techniques and their related algorithms. It addresses and compares different attributes of these methods, sketches in their possible advantages and disadvantages

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches