An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively

Dynamic spatio-temporal graph neural networks for hot topic prediction in scientific literature

With information explosion occurring in past decades, the rapid growth of papers published results in the rapid change of hot topics, especially in the biomedical domain. It turns out very hard for researchers who are interested in biomedical domain to track hot topics over time, as well as to predict the trends of them in the near future. Based on the above demand, it is important to have a model which is able to follow and predict the trend of hot topics continuously. Deep learning has been proven to be an efficient method to extract information from texts and use the information to predict the future trends. Under the thriving background of Deep Learning, Graph Neural Network (GNN) is able to capture the information from graph structures. There are various applications using GNN models, such as traffic flow prediction, chemical structure discovering, etc. In this research project, a dynamic spatio-temporal graph neural network is presented to keep track of the selected hot keywords and topics in the biomedical domain and predict the possible frequencies in the near future. The input of the model is obtained by extracting the monthly frequency information of selected keywords and topics from paper abstracts in PubMed, the largest biomedical literature collection. After training with data over a decade, the model is able to predict trends of selected hot keywords and topics in next 5 months. Thus, the presented model can help follow the trend of hot topics in the biomedical domain.Includes bibliographical reference

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Video Content Understanding Using Text

The rise of the social media and video streaming industry provided us a plethora of videos and their corresponding descriptive information in the form of concepts (words) and textual video captions. Due to the mass amount of available videos and the textual data, today is the best time ever to study the Computer Vision and Machine Learning problems related to videos and text. In this dissertation, we tackle multiple problems associated with the joint understanding of videos and text. We first address the task of multi-concept video retrieval, where the input is a set of words as concepts, and the output is a ranked list of full-length videos. This approach deals with multi-concept input and prolonged length of videos by incorporating multi-latent variables to tie the information within each shot (short clip of a full-video) and across shots. Secondly, we address the problem of video question answering, in which, the task is to answer a question, in the form of Fill-In-the-Blank (FIB), given a video. Answering a question is a task of retrieving a word from a dictionary (all possible words suitable for an answer) based on the input question and video. Following the FIB problem, we introduce a new problem, called Visual Text Correction (VTC), i.e., detecting and replacing an inaccurate word in the textual description of a video. We propose a deep network that can simultaneously detect an inaccuracy in a sentence while benefiting 1D-CNNs/LSTMs to encode short/long term dependencies, and fix it by replacing the inaccurate word(s). Finally, as the last part of the dissertation, we propose to tackle the problem of video generation using user input natural language sentences. Our proposed video generation method constructs two distributions out of the input text, corresponding to the first and last frames latent representations. We generate high-fidelity videos by interpolating latent representations and a sequence of CNN based up-pooling blocks

Multimodal sentiment analysis in real-life videos

This thesis extends the emerging field of multimodal sentiment analysis of real-life videos, taking two components into consideration: the emotion and the emotion's target. The emotion component of media is traditionally represented as a segment-based intensity model of emotion classes. This representation is replaced here by a value- and time-continuous view. Adjacent research fields, such as affective computing, have largely neglected the linguistic information available from automatic transcripts of audio-video material. As is demonstrated here, this text modality is well-suited for time- and value-continuous prediction. Moreover, source-specific problems, such as trustworthiness, have been largely unexplored so far. This work examines perceived trustworthiness of the source, and its quantification, in user-generated video data and presents a possible modelling path. Furthermore, the transfer between the continuous and discrete emotion representations is explored in order to summarise the emotional context at a segment level. The other component deals with the target of the emotion, for example, the topic the speaker is addressing. Emotion targets in a video dataset can, as is shown here, be coherently extracted based on automatic transcripts without limiting a priori parameters, such as the expected number of targets. Furthermore, alternatives to purely linguistic investigation in predicting targets, such as knowledge-bases and multimodal systems, are investigated. A new dataset is designed for this investigation, and, in conjunction with proposed novel deep neural networks, extensive experiments are conducted to explore the components described above. The developed systems show robust prediction results and demonstrate strengths of the respective modalities, feature sets, and modelling techniques. Finally, foundations are laid for cross-modal information prediction systems with applications to the correction of corrupted in-the-wild signals from real-life videos