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Learning-based approaches for human action recognition often rely on large training sets. Most of these approaches do not perform well when only a few training samples are available. In this paper, we consider the problem of human action recognition from a single clip per action. Each clip contains at most 25 frames. Using a patch based motion descriptor and matching scheme, we can achieve promising results on three different action datasets with a single clip as the template. Our results aredoi:10.1109/iccvw.2009.5457663 dblp:conf/iccvw/Yang0M09 fatcat:6ib727kbu5f5ddwsgozikmudhy