Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity

Networked Time Series Prediction with Incomplete Data

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%

BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis

Mixed reality applications require tracking the user's full-body motion to enable an immersive experience. However, typical head-mounted devices can only track head and hand movements, leading to a limited reconstruction of full-body motion due to variability in lower body configurations. We propose BoDiffusion -- a generative diffusion model for motion synthesis to tackle this under-constrained reconstruction problem. We present a time and space conditioning scheme that allows BoDiffusion to leverage sparse tracking inputs while generating smooth and realistic full-body motion sequences. To the best of our knowledge, this is the first approach that uses the reverse diffusion process to model full-body tracking as a conditional sequence generation task. We conduct experiments on the large-scale motion-capture dataset AMASS and show that our approach outperforms the state-of-the-art approaches by a significant margin in terms of full-body motion realism and joint reconstruction error

State of the Art on Diffusion Models for Visual Computing

The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literature on diffusion-based tools and applications has seen exponential growth and relevant papers are published across the computer graphics, computer vision, and AI communities with new works appearing daily on arXiv. This rapid growth of the field makes it difficult to keep up with all recent developments. The goal of this state-of-the-art report (STAR) is to introduce the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model, as well as overview important aspects of these generative AI tools, including personalization, conditioning, inversion, among others. Moreover, we give a comprehensive overview of the rapidly growing literature on diffusion-based generation and editing, categorized by the type of generated medium, including 2D images, videos, 3D objects, locomotion, and 4D scenes. Finally, we discuss available datasets, metrics, open challenges, and social implications. This STAR provides an intuitive starting point to explore this exciting topic for researchers, artists, and practitioners alike

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health

Learning visual representations with neural networks for video captioning and image generation

La recherche sur les réseaux de neurones a permis de réaliser de larges progrès durant la dernière décennie. Non seulement les réseaux de neurones ont été appliqués avec succès pour résoudre des problèmes de plus en plus complexes; mais ils sont aussi devenus l’approche dominante dans les domaines où ils ont été testés tels que la compréhension du langage, les agents jouant à des jeux de manière automatique ou encore la vision par ordinateur, grâce à leurs capacités calculatoires et leurs efficacités statistiques. La présente thèse étudie les réseaux de neurones appliqués à des problèmes en vision par ordinateur, où les représentations sémantiques abstraites jouent un rôle fondamental. Nous démontrerons, à la fois par la théorie et par l’expérimentation, la capacité des réseaux de neurones à apprendre de telles représentations à partir de données, avec ou sans supervision. Le contenu de la thèse est divisé en deux parties. La première partie étudie les réseaux de neurones appliqués à la description de vidéo en langage naturel, nécessitant l’apprentissage de représentation visuelle. Le premier modèle proposé permet d’avoir une attention dynamique sur les différentes trames de la vidéo lors de la génération de la description textuelle pour de courtes vidéos. Ce modèle est ensuite amélioré par l’introduction d’une opération de convolution récurrente. Par la suite, la dernière section de cette partie identifie un problème fondamental dans la description de vidéo en langage naturel et propose un nouveau type de métrique d’évaluation qui peut être utilisé empiriquement comme un oracle afin d’analyser les performances de modèles concernant cette tâche. La deuxième partie se concentre sur l’apprentissage non-supervisé et étudie une famille de modèles capables de générer des images. En particulier, l’accent est mis sur les “Neural Autoregressive Density Estimators (NADEs), une famille de modèles probabilistes pour les images naturelles. Ce travail met tout d’abord en évidence une connection entre les modèles NADEs et les réseaux stochastiques génératifs (GSN). De plus, une amélioration des modèles NADEs standards est proposée. Dénommés NADEs itératifs, cette amélioration introduit plusieurs itérations lors de l’inférence du modèle NADEs tout en préservant son nombre de paramètres. Débutant par une revue chronologique, ce travail se termine par un résumé des récents développements en lien avec les contributions présentées dans les deux parties principales, concernant les problèmes d’apprentissage de représentation sémantiques pour les images et les vidéos. De prometteuses directions de recherche sont envisagées.The past decade has been marked as a golden era of neural network research. Not only have neural networks been successfully applied to solve more and more challenging real- world problems, but also they have become the dominant approach in many of the places where they have been tested. These places include, for instance, language understanding, game playing, and computer vision, thanks to neural networks’ superiority in computational efficiency and statistical capacity. This thesis applies neural networks to problems in computer vision where high-level and semantically meaningful representations play a fundamental role. It demonstrates both in theory and in experiment the ability to learn such representations from data with and without supervision. The main content of the thesis is divided into two parts. The first part studies neural networks in the context of learning visual representations for the task of video captioning. Models are developed to dynamically focus on different frames while generating a natural language description of a short video. Such a model is further improved by recurrent convolutional operations. The end of this part identifies fundamental challenges in video captioning and proposes a new type of evaluation metric that may be used experimentally as an oracle to benchmark performance. The second part studies the family of models that generate images. While the first part is supervised, this part is unsupervised. The focus of it is the popular family of Neural Autoregressive Density Estimators (NADEs), a tractable probabilistic model for natural images. This work first makes a connection between NADEs and Generative Stochastic Networks (GSNs). The standard NADE is improved by introducing multiple iterations in its inference without increasing the number of parameters, which is dubbed iterative NADE. With a historical view at the beginning, this work ends with a summary of recent development for work discussed in the first two parts around the central topic of learning visual representations for images and videos. A bright future is envisioned at the end