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Open-set Action Recognition

Primary supervisor

Qiuhong Ke

Human action recognition plays an important role in various applications. Existing works assume that the training and testing share a common pre-defined list of action categories. Given a new unseen action during testing, the existing model will simply assign a wrong action category from the pre-defined list. This greatly limits the applicability of existing methods for practical model deployment.

Student cohort

Single Semester
Double Semester


This project aims to identify novel/unseen actions that the model does not train during model inference. In particular, the model should recognise the known action class and identify the novel actions.

Required knowledge

computer vision

deep learning