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Auflistung nach Autor:in "Lingenfelser, Florian"

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  • Zeitschriftenartikel
    eXplainable Cooperative Machine Learning with NOVA
    (KI - Künstliche Intelligenz: Vol. 34, No. 2, 2020) Baur, Tobias; Heimerl, Alexander; Lingenfelser, Florian; Wagner, Johannes; Valstar, Michel F.; Schuller, Björn; André, Elisabeth
    In the following article, we introduce a novel workflow, which we subsume under the term “explainable cooperative machine learning” and show its practical application in a data annotation and model training tool called NOVA . The main idea of our approach is to interactively incorporate the ‘human in the loop’ when training classification models from annotated data. In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce. A main aspect is the possibility of applying semi-supervised active learning techniques already during the annotation process by giving the possibility to pre-label data automatically, resulting in a drastic acceleration of the annotation process. Furthermore, the user-interface implements recent eXplainable AI techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanation. We show in an use-case evaluation that our workflow is able to speed up the annotation process, and further argue that by providing additional visual explanations annotators get to understand the decision making process as well as the trustworthiness of their trained machine learning models.
  • Zeitschriftenartikel
    Social Signal Interpretation (SSI)
    (KI - Künstliche Intelligenz: Vol. 25, No. 3, 2011) Wagner, Johannes; Lingenfelser, Florian; Bee, Nikolaus; André, Elisabeth
    The development of anticipatory user interfaces is a key issue in human-centred computing. Building systems that allow humans to communicate with a machine in the same natural and intuitive way as they would with each other requires detection and interpretation of the user’s affective and social signals. These are expressed in various and often complementary ways, including gestures, speech, mimics etc. Implementing fast and robust recognition engines is not only a necessary, but also challenging task. In this article, we introduce our Social Signal Interpretation (SSI) tool, a framework dedicated to support the development of such online recognition systems. The paper at hand discusses the processing of four modalities, namely audio, video, gesture and biosignals, with focus on affect recognition, and explains various approaches to fuse the extracted information to a final decision.