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We propose a new action classification model and a novel iterative alignment approach to address weakly supervised action localization task that only require the transcript to learn the exact time of actions.
Our supervised action recognition approach, outlined in Section 5, is based on SVM.
For supervised action recognition, we use as a learning algorithm an SVM with the characteristic kernel, introduced in Example.
In this paper, we are applying the theoretical findings reported in the previous sections to two different problems: unsupervised and supervised action recognition.
Figure 2 For supervised action recognition, for each sequence we compute a single feature vector (left) representing histogram-based data, as described in Section 6.
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- Supervised and semi-supervised training – We distinguish between supervised and semi-supervised training.
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We study unsupervised and supervised recognition of human actions in video sequences.
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