TV News Story Segmentation Based on Semantic Coherence and Content Similarity

In this paper, we introduce and evaluate two novel approaches, one using video stream and the other using close-caption text stream, for segmenting TV news into stories. The segmentation of the video stream into stories is achieved by detecting anchor person shots and the text stream is segmented into stories using a Latent Dirichlet Allocation (LDA) based approach. The benefit of the proposed LDA based approach is that along with the story segmentation it also provides the topic distribution associated with each segment. We evaluated our techniques on the TRECVid 2003 benchmark database and found that though the individual systems give comparable results, a combination of the outputs of the two systems gives a significant improvement over the performance of the individual systems

Multi-Modal Deep Learning to Understand Vision and Language

Developing intelligent agents that can perceive and understand the rich visual world around us has been a long-standing goal in the field of artificial intelligence. In the last few years, significant progress has been made towards this goal and deep learning has been attributed to recent incredible advances in general visual and language understanding. Convolutional neural networks have been used to learn image representations while recurrent neural networks have demonstrated the ability to generate text from visual stimuli. In this thesis, we develop methods and techniques using hybrid convolutional and recurrent neural network architectures that connect visual data and natural language utterances. Towards appreciating these methods, this work is divided into two broad groups. Firstly, we introduce a general purpose attention mechanism modeled using a continuous function for video understanding. The use of an attention based hierarchical approach along with automatic boundary detection advances state-of-the-art video captioning results. We also develop techniques for summarizing and annotating long videos. In the second part, we introduce architectures along with training techniques to produce a common connection space where natural language sentences are efficiently and accurately connected with visual modalities. In this connection space, similar concepts lie close, while dissimilar concepts lie far apart, irrespective` of their modality. We discuss four modality transformations: visual to text, text to visual, visual to visual and text to text. We introduce a novel attention mechanism to align multi-modal embeddings which are learned through a multi-modal metric loss function. The common vector space is shown to enable bidirectional generation of images and text. The learned common vector space is evaluated on multiple image-text datasets for cross-modal retrieval and zero-shot retrieval. The models are shown to advance the state-of-the-art on tasks that require joint processing of images and natural language

TempCLR: Temporal Alignment Representation with Contrastive Learning

Video representation learning has been successful in video-text pre-training for zero-shot transfer, where each sentence is trained to be close to the paired video clips in a common feature space. For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly. However, such unit-level similarity measure may ignore the global temporal context over a long time span, which inevitably limits the generalization ability. In this paper, we propose a contrastive learning framework TempCLR to compare the full video and the paragraph explicitly. As the video/paragraph is formulated as a sequence of clips/sentences, under the constraint of their temporal order, we use dynamic time warping to compute the minimum cumulative cost over sentence-clip pairs as the sequence-level distance. To explore the temporal dynamics, we break the consistency of temporal order by shuffling the video clips or sentences according to the temporal granularity. In this way, we obtain the representations for clips/sentences, which perceive the temporal information and thus facilitate the sequence alignment. In addition to pre-training on the video and paragraph, our approach can also generalize on the matching between different video instances. We evaluate our approach on video retrieval, action step localization, and few-shot action recognition, and achieve consistent performance gain over all three tasks. Detailed ablation studies are provided to justify the approach design

Audio-visual Self-Supervised Representation Learning in-the-wild

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Επιστήμη Δεδομένων και Μηχανική Μάθηση

Temporal Sentence Grounding in Videos: A Survey and Future Directions

Temporal sentence grounding in videos (TSGV), \aka natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video. Connecting computer vision and natural language, TSGV has drawn significant attention from researchers in both communities. This survey attempts to provide a summary of fundamental concepts in TSGV and current research status, as well as future research directions. As the background, we present a common structure of functional components in TSGV, in a tutorial style: from feature extraction from raw video and language query, to answer prediction of the target moment. Then we review the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities. We construct a taxonomy of TSGV techniques and elaborate the methods in different categories with their strengths and weaknesses. Lastly, we discuss issues with the current TSGV research and share our insights about promising research directions.Comment: 29 pages, 32 figures, 9 table

Video Categorization Using Data Mining

Video categorization using data mining is the area of the research that aims to propose adeveloped method based on Artificial Neural Network (ANN), which could be used to classify video files into different categories according to the content. In order to test this method, the classifications of video files are discussed. The applied system proposes that the video could be categorized in two classes. The first one is educational while is noneducational. The classification is conducted based on the motion using optical flow. Several experiments were conducted using Artificial Neural Network (ANN) model. The research facilitate access to the required educational video to the learners students, especially novice students. This research objective is to investigate how the effect of motion feature can be useful in such lassification. We believe that other effects such audio features, text features, and other factors can enhance accuracy, but this requires wider studies and need more time. The accuracy of results in video classification to educational and non-educational through technique 3 fold cross validation and using (ANN) model is 54%. This result may can be improved by introducing other factors mentioned above

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

Semantics for vision-and-language understanding

Recent advancements in Artificial Intelligence have led to several breakthroughs in many heterogeneous scientific fields, such as the prediction of protein structures or self-driving cars. These results are obtained by means of Machine Learning techniques, which make it possible to automatically learn from the available annotated examples a mathematical model capable of solving the task. One of its sub-fields, Deep Learning, brought further improvements by providing the possibility to also compute an informative and non-redundant representation for each example by means of the same learning process. To successfully solve the task under analysis, the model needs to overcome the generalization gap, meaning that it needs to work well both on the training data, and on examples which are drawn from the same distribution but are never observed at training time. Several heuristics are often used to overcome this gap, such as the introduction of inductive biases when modeling the data or the usage of regularization techniques; however, a popular way consists in collecting and annotating more examples hoping they can cover the cases which were not previously observed. In particular, recent state-of-the-art solutions use hundreds of millions or even billions of annotated examples, and the underlying trend seems to imply that the collection and annotation of more and more examples should be the prominent way to overcome the generalization gap. However, there are many fields, e.g. medical fields, in which it is difficult to collect such a large amount of examples, and producing high quality annotations is even more arduous and costly. During my Ph.D. and in this thesis, I designed and proposed several solutions which address the generalization gap in three different domains by leveraging semantic aspects of the available data. In particular, the first part of the thesis includes techniques which create new annotations for the data under analysis: these include data augmentation techniques, which are used to compute variations of the annotations by means of semantics-preserving transformations, and transfer learning, which is used in the scope of this thesis to automatically generate textual descriptions for a set of images. In the second part of the thesis, this gap is reduced by customizing the training objective based on the semantics of the annotations. By means of these customizations, a problem is shifted from the commonly used single-task setting to a multi-task learning setting by designing an additional task, and then two variations of a standard loss function are proposed by introducing semantic knowledge into the training process

Self-supervised Face Representation Learning

This thesis investigates fine-tuning deep face features in a self-supervised manner for discriminative face representation learning, wherein we develop methods to automatically generate pseudo-labels for training a neural network. Most importantly solving this problem helps us to advance the state-of-the-art in representation learning and can be beneficial to a variety of practical downstream tasks. Fortunately, there is a vast amount of videos on the internet that can be used by machines to learn an effective representation. We present methods that can learn a strong face representation from large-scale data be the form of images or video. However, while learning a good representation using a deep learning algorithm requires a large-scale dataset with manually curated labels, we propose self-supervised approaches to generate pseudo-labels utilizing the temporal structure of the video data and similarity constraints to get supervision from the data itself. We aim to learn a representation that exhibits small distances between samples from the same person, and large inter-person distances in feature space. Using metric learning one could achieve that as it is comprised of a pull-term, pulling data points from the same class closer, and a push-term, pushing data points from a different class further away. Metric learning for improving feature quality is useful but requires some form of external supervision to provide labels for the same or different pairs. In the case of face clustering in TV series, we may obtain this supervision from tracks and other cues. The tracking acts as a form of high precision clustering (grouping detections within a shot) and is used to automatically generate positive and negative pairs of face images. Inspired from that we propose two variants of discriminative approaches: Track-supervised Siamese network (TSiam) and Self-supervised Siamese network (SSiam). In TSiam, we utilize the tracking supervision to obtain the pair, additional we include negative training pairs for singleton tracks -- tracks that are not temporally co-occurring. As supervision from tracking may not always be available, to enable the use of metric learning without any supervision we propose an effective approach SSiam that can generate the required pairs automatically during training. In SSiam, we leverage dynamic generation of positive and negative pairs based on sorting distances (i.e. ranking) on a subset of frames and do not have to only rely on video/track based supervision. Next, we present a method namely Clustering-based Contrastive Learning (CCL), a new clustering-based representation learning approach that utilizes automatically discovered partitions obtained from a clustering algorithm (FINCH) as weak supervision along with inherent video constraints to learn discriminative face features. As annotating datasets is costly and difficult, using label-free and weak supervision obtained from a clustering algorithm as a proxy learning task is promising. Through our analysis, we show that creating positive and negative training pairs using clustering predictions help to improve the performance for video face clustering. We then propose a method face grouping on graphs (FGG), a method for unsupervised fine-tuning of deep face feature representations. We utilize a graph structure with positive and negative edges over a set of face-tracks based on their temporal structure of the video data and similarity-based constraints. Using graph neural networks, the features communicate over the edges allowing each track\u27s feature to exchange information with its neighbors, and thus push each representation in a direction in feature space that groups all representations of the same person together and separates representations of a different person. Having developed these methods to generate weak-labels for face representation learning, next we propose to learn compact yet effective representation for describing face tracks in videos into compact descriptors, that can complement previous methods towards learning a more powerful face representation. Specifically, we propose Temporal Compact Bilinear Pooling (TCBP) to encode the temporal segments in videos into a compact descriptor. TCBP possesses the ability to capture interactions between each element of the feature representation with one-another over a long-range temporal context. We integrated our previous methods TSiam, SSiam and CCL with TCBP and demonstrated that TCBP has excellent capabilities in learning a strong face representation. We further show TCBP has exceptional transfer abilities to applications such as multimodal video clip representation that jointly encodes images, audio, video and text, and video classification. All of these contributions are demonstrated on benchmark video clustering datasets: The Big Bang Theory, Buffy the Vampire Slayer and Harry Potter 1. We provide extensive evaluations on these datasets achieving a significant boost in performance over the base features, and in comparison to the state-of-the-art results