Tobias Dencker

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This paper is concerned with a Bayesian approach of actively selecting camera parameters in order to recognize a given object from a finite set of object classes. Gaussian process regression is applied to learn the likelihood of image features given the object classes and camera parameters. In doing so, the object recognition task can be treated as Bayesian(More)
Complementary to ablation studies in Section 4.2, we study the sensitivity of the temporal ordering task to different configurations. In particular, we vary the ranges τ, τ− for sampling positive and negative pairs as describe in Section 3.1 and train a network using only the modified temporal ordering task. Table 1 shows that the best setting for the two(More)
Human pose analysis is presently dominated by deep convolutional networks trained with extensive manual annotations of joint locations and beyond. To avoid the need for expensive labeling, we exploit spatiotemporal relations in training videos for self-supervised learning of pose embeddings. The key idea is to combine temporal ordering and spatial placement(More)
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