Romain Vuillemot bio photo

Romain Vuillemot

Assistant Professor
École Centrale de Lyon
LIRIS Laboratory

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Boundary Objects in Design Studies: Reflections on the Collaborative Creation of Isochrone Maps

Romain Vuillemot, Philippe Rivière, Anaëlle Beignon, Aurélien Tabard

Proceedings: EuroVis'21 (Computer Graphics Forum)

We propose to take an artifact-centric approach to design studies by leveraging the concept of boundary object. Design studies typically focus on processes and articulate design decisions in a project-specific context with a goal of transferability. We argue that design studies could benefit from paying attention to the material conditions in which teams collaborate to reach design outcomes. We report on a design study of isochrone maps following cartographic generalization principles. Focusing on boundary objects enables us to characterize five categories of artifacts and tools that facilitated collaboration between actors involved in the design process (structured collections, structuring artifacts, process-centric artifacts, generative artifacts, and bridging artifacts). We found that artifacts such as layered maps and map collections played a unifying role for our inter-disciplinary team. We discuss how such artifacts can be pivotal in the design process. Finally, we discuss how considering boundary objects could improve the transferability of design study results, and support reflection on inter-disciplinary collaboration in the domain of Information Visualization.


Visual Question Answering systems target answering open-ended textual questions given input images. They are a testbed for learning high-level reasoning with a primary use in HCI, for instance assistance for the visually impaired. Recent research has shown that state-of-the-art models tend to produce answers exploiting biases and shortcuts in the training data, and sometimes do not even look at the input image, instead of performing the required reasoning steps. We present VisQA, a visual analytics tool that explores this question of reasoning vs. bias exploitation. It exposes the key element of state-of-the-art neural models — attention maps in transformers. Our working hypothesis is that reasoning steps leading to model predictions are observable from attention distributions, which are particularly useful for visualization. The design process of VisQA was motivated by well-known bias examples from the fields of deep learning and vision-language reasoning and evaluated in two ways. First, as a result of a collaboration of three fields, machine learning, vision and language reasoning, and data analytics, the work lead to a direct impact on the design and training of a neural model for VQA, improving model performance as a consequence. Second, we also report on the design of VisQA, and a goal-oriented evaluation of VisQA targeting the analysis of a model decision process from multiple experts, providing evidence that it makes the inner workings of models accessible to users.

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