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A visual language to characterise transitions in narrative visualization
We use a taxonomy of panel-to-panel transitions in comics, refined the definition of its components to reflect the nature of data-stories in information visualization, and then, use the taxonomy in coding a number of VAST challenges videos from the last four years. We represent the use of transitions in each video graphically with a diagram that shows how the information was added incrementally in order to tell a story that answers a particular question. A number of issues have been taken into account when coding transitions in each video as well as in designing and creating the visual diagram such as, nested transitions, the use of sub-topics, and delayed transitions
A semantic and language-based representation of an environmental scene
The modeling of a landscape environment is a cognitive activity that requires appropriate spatial representations. The research presented in this paper introduces a structural and semantic categorization of a landscape view based on panoramic photographs that act as a substitute of a given natural environment. Verbal descriptions of a landscape scene provide themodeling input of our approach. This structure-based model identifies the spatial, relational, and semantic constructs that emerge from these descriptions. Concepts in the environment are qualified according to a semantic classification, their proximity and direction to the observer, and the spatial relations that qualify them. The resulting model is represented in a way that constitutes a modeling support for the study of environmental scenes, and a contribution for further research oriented to the mapping of a verbal description onto a geographical information system-based representation
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
Recognising objects according to a pre-defined fixed set of class labels has
been well studied in the Computer Vision. There are a great many practical
applications where the subjects that may be of interest are not known
beforehand, or so easily delineated, however. In many of these cases natural
language dialog is a natural way to specify the subject of interest, and the
task achieving this capability (a.k.a, Referring Expression Comprehension) has
recently attracted attention. To this end we propose a unified framework, the
ParalleL AttentioN (PLAN) network, to discover the object in an image that is
being referred to in variable length natural expression descriptions, from
short phrases query to long multi-round dialogs. The PLAN network has two
attention mechanisms that relate parts of the expressions to both the global
visual content and also directly to object candidates. Furthermore, the
attention mechanisms are recurrent, making the referring process visualizable
and explainable. The attended information from these dual sources are combined
to reason about the referred object. These two attention mechanisms can be
trained in parallel and we find the combined system outperforms the
state-of-art on several benchmarked datasets with different length language
input, such as RefCOCO, RefCOCO+ and GuessWhat?!.Comment: 11 page
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