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We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network...
In speech recognition system, an improved multi-base neural network speech recognition model is proposed to solve the problem of long learning time and slow convergence rate of deep neural network. However, the improved model introduces a large number of parameters in the training process to make the model over-fitted in the test set, resulting in the deterioration of generalization ability and the...
In this paper, we tackle the problem of understanding the temporal structure of complex events in highly varying videos obtained from the Internet. Towards this goal, we utilize a conditional model trained in a max-margin framework that is able to automatically discover discriminative and interesting segments of video, while simultaneously achieving competitive accuracies on difficult detection and...
Anaphora resolution plays an important role in nature language processing. According to features of Chinese personal pronoun, we present an approach which adopts Conditional Random Fields to do anaphora resolution of personal pronoun in Chinese texts. The method takes into account all kinds of anaphoric features and those effects among each other. The experimental results on the Chinese ACE training...
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