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This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning...
Huge amounts of audiovisual material have been digitized recently, resulting in a great source of information relevant both from a cultural and historical point of view. However, in spite of millions of man hours spent on manual annotation and recent advances in (semi-)automatic metadata generation, accessing these archives and retrieving relevant information from them remains a difficult task. Up...
A face-naming method that learns from labeled and unlabeled examples relies on iterative label propagation in a graph of connected faces or name–face pairs.
Labeling persons appearing in video frames with names detected from the video transcript helps improving the video content identification and search task. We develop a face naming method that learns from labeled and unlabeled examples using iterative label propagation in a graph of connected faces or name-face pairs. The advantage of this method is that it can use very few labeled data points and...
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