In this paper two sets of evaluation experiments are conducted. First, we compare state-of-the-art automatic music genre classification algorithms to human performance on the same dataset, via a listening experiment. This will show that the improvements of contentbased systems over the last years have reduced the gap between automatic and human classification performance, but could not yet clos...

Static magnetic resonance imaging partially resolves soft tissue details of the oropharynx, which are crucial in swallowing and speech studies. However, delineation of tongue tissue remains a challenge due to the lack of definitive boundary features. In this article, we propose a minimally interactive inter-subject mesh-to-image registration scheme to tackle 3D segmentation of the human tongue ...

Most current vision algorithms deliver their output ‘as is’, without indicating whether it is correct or not. In this paper we propose evaluator algorithms that predict if a vision algorithm has succeeded. We illustrate this idea for the case of Human Pose Estimation (HPE). We describe the stages required to learn and test an evaluator, including the use of an annotated ground truth dataset for...

We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild. We leverage the natural synchronization between vision and sound to learn an acoustic representation using two-million unlabeled videos. Unlabeled video has the advantage that it can be economically acquired at massive scales, yet contains useful signals about natural soun...

Abstract We introduce and study some variants of a notion canonical set theoretical truth. By this, we mean truth in transitive proper class model M ZFC that is uniquely characterized by $$\in$$ ∈ -formula. show there are interesting statements hold all such models, but do not follow from ZFC, as the ground a...