Sequence to Sequence -- Video to Text

Real-world videos often have complex dynamics; and methods for generating open-domain video descriptions should be sensitive to temporal structure and allow both input (sequence of frames) and output (sequence of words) of variable length. To approach this problem, we propose a novel end-to-end sequence-to-sequence model to generate captions for videos. For this we exploit recurrent neural networks, specifically LSTMs, which have demonstrated state-of-the-art performance in image caption generation. Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip. Our model naturally is able to learn the temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model. We evaluate several variants of our model that exploit different visual features on a standard set of YouTube videos and two movie description datasets (M-VAD and MPII-MD).Comment: ICCV 2015 camera-ready. Includes code, project page and LSMDC challenge result

Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking

Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adversarial Network architecture for person localisation, which overcomes issues related to occlusions and noisy detections, typically found in a multi person environment. In the proposed tracking framework we build upon recent advances in pedestrian trajectory prediction approaches and propose a novel data association scheme based on predicted trajectories. This removes the need for computationally expensive person re-identification systems based on appearance features and generates human like trajectories with minimal fragmentation. The proposed method is evaluated on multiple public benchmarks including both static and dynamic cameras and is capable of generating outstanding performance, especially among other recently proposed deep neural network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer Vision (WACV), 201

Movie Description

Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. In total the Large Scale Movie Description Challenge (LSMDC) contains a parallel corpus of 118,114 sentences and video clips from 202 movies. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are indeed more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in a challenge organized in the context of the workshop "Describing and Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at ICCV 2015

Deep Architectures for Visual Recognition and Description

In recent times, digital media contents are inherently of multimedia type, consisting of the form text, audio, image and video. Several of the outstanding computer Vision (CV) problems are being successfully solved with the help of modern Machine Learning (ML) techniques. Plenty of research work has already been carried out in the field of Automatic Image Annotation (AIA), Image Captioning and Video Tagging. Video Captioning, i.e., automatic description generation from digital video, however, is a different and complex problem altogether. This study compares various existing video captioning approaches available today and attempts their classification and analysis based on different parameters, viz., type of captioning methods (generation/retrieval), type of learning models employed, the desired output description length generated, etc. This dissertation also attempts to critically analyze the existing benchmark datasets used in various video captioning models and the evaluation metrics for assessing the final quality of the resultant video descriptions generated. A detailed study of important existing models, highlighting their comparative advantages as well as disadvantages are also included. In this study a novel approach for video captioning on the Microsoft Video Description (MSVD) dataset and Microsoft Video-to-Text (MSR-VTT) dataset is proposed using supervised learning techniques to train a deep combinational framework, for achieving better quality video captioning via predicting semantic tags. We develop simple shallow CNN (2D and 3D) as feature extractors, Deep Neural Networks (DNNs and Bidirectional LSTMs (BiLSTMs) as tag prediction models and Recurrent Neural Networks (RNNs) (LSTM) model as the language model. The aim of the work was to provide an alternative narrative to generating captions from videos via semantic tag predictions and deploy simpler shallower deep model architectures with lower memory requirements as solution so that it is not very memory extensive and the developed models prove to be stable and viable options when the scale of the data is increased. This study also successfully employed deep architectures like the Convolutional Neural Network (CNN) for speeding up automation process of hand gesture recognition and classification of the sign languages of the Indian classical dance form, ‘Bharatnatyam’. This hand gesture classification is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from (i) Google search engine (Google images), (ii) YouTube videos (dynamic and with background considered) and (iii) professional artists under staged environment constraints (plain backgrounds). 2) exploring the effectiveness of CNNs for identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored for achieving higher classification accuracy

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

Deep Architectures for Content Moderation and Movie Content Rating

Rating a video based on its content is an important step for classifying video age categories. Movie content rating and TV show rating are the two most common rating systems established by professional committees. However, manually reviewing and evaluating scene/film content by a committee is a tedious work and it becomes increasingly difficult with the ever-growing amount of online video content. As such, a desirable solution is to use computer vision based video content analysis techniques to automate the evaluation process. In this paper, related works are summarized for action recognition, multi-modal learning, movie genre classification, and sensitive content detection in the context of content moderation and movie content rating. The project page is available at https://github.com/fcakyon/content-moderation-deep-learning