Dynamic Visual Abstraction of Soccer Movement

Trajectory-based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on-the-fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi-automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer

Visual Abstraction and Reasoning through Language

While Artificial Intelligence (AI) models have achieved human or even superhuman performance in narrowly defined applications, they still struggle to show signs of broader and more flexible intelligence. The Abstraction and Reasoning Corpus (ARC), introduced by Fran\c{c}ois Chollet, aims to assess how close AI systems are to human-like cognitive abilities. Most current approaches rely on carefully handcrafted domain-specific languages (DSLs), which are used to brute-force solutions to the tasks present in ARC. In this work, we propose a general framework for solving ARC based on natural language descriptions of the tasks. While not yet beating state-of-the-art DSL models on ARC, we demonstrate the immense potential of our approach hinted at by the ability to solve previously unsolved tasks.Comment: The first two authors have contributed equally to this work. Accepted as regular paper at CVPR 2023 Workshop and Challenges for New Frontiers in Visual Language Reasoning: Compositionality, Prompts and Causality (NFVLR

Visual abstraction for games on large public displays

From its earliest developments video game design has arguably been closely coupled to technological evolution particularly in relation to graphics. In very early games the limitations of technology led to highly abstracted graphics but as technology improved, abstraction has largely been left behind as developers strive towards ever-greater realism. Thus, games are generally drawing from conventions established in the mediums of film and television, and potentially limiting themselves from the possibilities abstraction may offer. In this research, we consider whether highly abstracted graphics are perceived as detrimental to gameplay and learnability by current gamers through the creation of a game using very low-resolution display that would accommodate a range of display options in a playable city. The results of trialing the game at a citywide light festival event where it was played by over 150 people indicated that abstraction made little difference to their sense of engagement with the game, however it did foster communication between players and suggests abstraction is a viable game design option for playable city displays

Re-assemblage of forms

This body of work investigates abstraction, process, and the construction of psychological spaces. This paper will define specific works from the overall body of work, the formal processes of drawing and collage, and the visual abstraction of the drawings and their origins within my personal history

Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks

Artificial agents today can answer factual questions. But they fall short on questions that require common sense reasoning. Perhaps this is because most existing common sense databases rely on text to learn and represent knowledge. But much of common sense knowledge is unwritten - partly because it tends not to be interesting enough to talk about, and partly because some common sense is unnatural to articulate in text. While unwritten, it is not unseen. In this paper we leverage semantic common sense knowledge learned from images - i.e. visual common sense - in two textual tasks: fill-in-the-blank and visual paraphrasing. We propose to "imagine" the scene behind the text, and leverage visual cues from the "imagined" scenes in addition to textual cues while answering these questions. We imagine the scenes as a visual abstraction. Our approach outperforms a strong text-only baseline on these tasks. Our proposed tasks can serve as benchmarks to quantitatively evaluate progress in solving tasks that go "beyond recognition". Our code and datasets are publicly available

In two minds: executive functioning versus theory of mind in behavioural variant frontotemporal dementia

Background: The relationship of executive function (EF) and theory of mind (ToM) deficits in neurodegeneration is still debated. There is contradicting evidence as to whether these cognitive processes are overlapping or distinct, which has clear clinical relevance for the evaluation of their associated clinical symptoms. Aim: To investigate the relationship of EF and ToM deficits via a data-driven approach in a large sample of patients with behavioural variant frontotemporal dementia (bvFTD). Methods: Data of 46 patients with bvFTD were employed in a hierarchical cluster analysis to determine the similarity of variance between different EF measures (verbal abstraction, verbal initiation, motor programming, sensitivity to interference, inhibitory control, visual abstraction, flexibility, working memory/attention) and ToM (faux pas). Results: Overall results showed that EF measures were clustered separately from the ToM measure. A post hoc analysis revealed a more complex picture where selected ToM subcomponents (empathy; intention) showed a relationship to specific EF measures (verbal abstraction; working memory/attention), whereas the remaining EF and ToM subcomponents were separate. Conclusions: Taken together, these findings suggest that EF and ToM are distinct components; however, ToM empathy and intention subcomponents might share some functions with specific EF processes. This has important implications for guiding diagnostic assessment of these deficits in clinical conditions