Zero-shot Learning: Recognition, Tagging, and Detection of Novel Concepts
Recent advancements in deep neural networks have performed favourably well on the supervised object recognition task. Towards an ultimate automated visual recognition system, we identify three key shortcomings of the existing supervised learning approaches. First, the dependency on a significantly large volume of manually annotated examples (e.g., ImageNet dataset with ~10 million images) limits the scalability of deep networks. Second, once the model learning stage is complete, it is difficult...[Show more]
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