Predicting Video Characteristics with Generative Adversarial Networks


Predicting Video Characteristics with Generative Adversarial Networks – Visual object recognition (VA) has attracted significant interest due to its vast range of applications. However, the proposed approach is based on using low rank embedding models to solve the visual representation problem. In this work, we propose a novel low rank embedding learning framework for VA by using variational inference (VLI) in the VLI space to automatically generate low rank embeddings for visual objects. We propose an online variational inference scheme for embedding the posterior of a convolutional neural network in the VLI space. The proposed approach is formulated as a convolutional neural network (CNN) for VA which learns to infer the vignetting probability score of the convolutional network. This is performed using a single CNN as input to the VLI network. We demonstrate that this approach outperformed the state-of-the-art methods for VA on the IJBVA benchmark.

We present a novel method for understanding temporal ambiguity in the wild. The proposed model is a neural network trained to predict the current tense state of a language user’s speech, or a sequence of sentences. As the user’s speech becomes more and more important (i.e., more relevant to the current tense state), this is an opportunity for the user to improve his or her understanding of the language’s tense state. An automatic learning tool, we call Temporal Context Knowledge (TCK), is used to predict the next tense state of a user’s speech to achieve a more detailed understanding of the current tense state. Our model combines the temporal context knowledge from the user and the semantic content in his or her speech into the state-action tree. We build an automatic and robust neural network model to predict the current tense state of user’s speech using the knowledge extracted by our neural network. Experiments are conducted using the MIMI dataset and on two different languages. Results show that our model outperforms current state-action learning methods for predicting the current tense state of users by a large margin.

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Predicting Video Characteristics with Generative Adversarial Networks

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  • On the Performance of Deep Convolutional Neural Networks in Semi-Supervised Learning of Point Clouds

    Exploring Temporal Context Knowledge for Real-time, Multi-lingual Conversational SearchWe present a novel method for understanding temporal ambiguity in the wild. The proposed model is a neural network trained to predict the current tense state of a language user’s speech, or a sequence of sentences. As the user’s speech becomes more and more important (i.e., more relevant to the current tense state), this is an opportunity for the user to improve his or her understanding of the language’s tense state. An automatic learning tool, we call Temporal Context Knowledge (TCK), is used to predict the next tense state of a user’s speech to achieve a more detailed understanding of the current tense state. Our model combines the temporal context knowledge from the user and the semantic content in his or her speech into the state-action tree. We build an automatic and robust neural network model to predict the current tense state of user’s speech using the knowledge extracted by our neural network. Experiments are conducted using the MIMI dataset and on two different languages. Results show that our model outperforms current state-action learning methods for predicting the current tense state of users by a large margin.


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