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Activity and event recognition from video has utilized low-level features over higher-level text-based class attributes and ontologies because they traditionally have been more effective on small datasets. However, by including human knowledge-driven associations between actions and attributes while recognizing the lower-level attributes with their temporal relationships, we can learn a much greater...
When learning a new classifier, poor quality training data can significantly degrade performance. Applying selection conditions to the training data can prevent mislabeled, noisy, or damaged data from skewing the classifier. We extend a set of action attributes and apply training case attribute selection conditions to a challenging action recognition dataset.
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