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Towards ontology driven learning of visual concept detectors
[article]
2016
arXiv
pre-print
The maturity of deep learning techniques has led in recent years to a breakthrough in object recognition in visual media. While for some specific benchmarks, neural techniques seem to match if not outperform human judgement, challenges are still open for detecting arbitrary concepts in arbitrary videos. In this paper, we propose a system that combines neural techniques, a large scale visual concepts ontology, and an active learning loop, to provide on the fly model learning of arbitrary
arXiv:1605.09757v1
fatcat:pkway76tcrh3xpngzogls4vwqi