Watch, Ask, Learn, and Improve: a lifelong learning cycle for visual recognition

Christoph Käding, Erik Rodner, Alexander Freytag, Joachim Denzler
2016 The European Symposium on Artificial Neural Networks  
We present WALI, a prototypical system that learns object categories over time by continuously watching online videos. WALI actively asks questions to a human annotator about the visual content of observed video frames. Thereby, WALI is able to receive information about new categories and to simultaneously improve its generalization abilities. The functionality of WALI is driven by scalable active learning, efficient incremental learning, as well as state-of-the-art visual descriptors. In our
more » ... periments, we show qualitative and quantitative statistics about WALI's learning process. WALI runs continuously and regularly asks questions.
dblp:conf/esann/KadingRFD16 fatcat:qg5czg4z65auvgpsemhwo7djgm