Video description aims to generate descriptive natural language for videos.Inspired from the deep neural network(DNN) used in the machine translation,the video description(VD) task applies the convolutional neural network(CNN) to extracting video features and the long short-term memory(LSTM) to generating descriptions.However,some models generate incorrect words and syntax.The reason may because that the previous models only apply LSTM to generate sentences,which learn insufficient linguistic information.In order to solve this problem,an end-to-end DNN model incorporated subject,verb and object(SVO) supervision is proposed.Experimental results on a publicly available dataset,i.e.Youtube2 Text,indicate that our model gets a 58.4% consensus-based image description evaluation(CIDEr) value.It outperforms the mean pool and video description with first feed(VD-FF) models,demonstrating the effectiveness of SVO supervision.
Utilizing a three-particle W state, we come up with a protocol for the teleportation of an unknown two-particle entangled state. It is shown that the teleportation can be deterministically and exactly realized. Moreover, two-particle entanglement teleportation is generalized to a system consisting of many particles via a three-particle W state and a multi-particle W state, respectively. All unitary transformations performed by the receiver are given in a concise formula.
