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Transductive Transfer Learning for Action Recognition
AbstractIn video processing, it is often the case that one video may contain several samples of actions that share a number of low level features (eg illumination, motion style, etc). Therefore, each video constitutes a domain. We show that, for action classification, a classifier that is trained with one video may lead to poor performance when applied to another video, but if the unlabelled samples of this other video are available, it is possible to apply a transductive transfer learning technique, leading to a significant improvement in performance.
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