Video Classification via Weakly Supervised Sequence Modeling
We proposed a multiple-instance learning algorithm to model sequential data, with novel conditional likelihood formulation. We incorporated temporal correlations into MIL for video classification, by explicitly exploiting the chain CRFs model. Our method showed superior performance on both synthetic data and realistic dataset on action and gesture recognition.
video classification weakly supervised sequence modeling action gesture multiple-instance learning (mil) conditional random fields (crfs)