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Many computer vision problems are naturally formulated as random fields, specifically MRFs or CRFs. The introduction of graph cuts has enabled efficient and optimal inference in associative random fields, greatly advancing applications such as segmentation, stereo reconstruction and many others. However, while fast inference is now widespread, parameter learning in random fields has remained an intractable...
We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition...
The focus of this work is speech synthesis tailored to the needs of spoken dialogue systems. More specifically, the framework of HMM-based speech synthesis is utilized to train an emphatic voice that also considers dialogue context for decision tree state clustering. To achieve this, we designed and recorded a speech corpus comprising system prompts from human-computer interaction, as well as additional...
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