On the Role of Constraints in Stochastic Matching and Stratified Search – We present a method to recognize the most probable or non-obvious target of a given sequence of words, a common pattern of human attention has been used to perform many applications of the model, including the extraction of syntactic information for a sequence of words and its relation to the meaning associated with that sequence. Despite its effectiveness, there is substantial work still to be done on such recognition and on a variety of models, notably the CNN-HMM model. In this work we generalize the CNN-HMM model to a new model with different performance measures.
With the rapid success of deep-learned multimodal face recognition, it was challenging to provide an effective framework for this task. In this work we first develop an efficient framework for multimodal face recognition (MOBIR) that leverages the knowledge from deep-learned face recognition (DHR). In order for this framework to scale to new datasets, we provide an extensive set of deep-learned DHR datasets with the goal of providing a deep-learning framework for MOBIR. We evaluate our framework on several benchmark datasets. The framework has outperformed the state-of-the-art approaches, which uses deep models and deep representations to represent the recognition results. Moreover, our framework is particularly good at representing the different types of face, which is a difficult task for face recognition practitioners because of the complex face context information. We then apply the MOBIR framework to the context labeling as well as face classification and pose estimation tasks using deep neural networks (DNNs) in order to achieve state-of-the-art results on our benchmark dataset.
Sparsity Regularized Generalized Recurrent Neural Networks
Show and Tell: Learning to Watch from Text Videos
On the Role of Constraints in Stochastic Matching and Stratified Search
Learning a Universal Metric for Interpretability
Matching Networks for Robust Emotion RecognitionWith the rapid success of deep-learned multimodal face recognition, it was challenging to provide an effective framework for this task. In this work we first develop an efficient framework for multimodal face recognition (MOBIR) that leverages the knowledge from deep-learned face recognition (DHR). In order for this framework to scale to new datasets, we provide an extensive set of deep-learned DHR datasets with the goal of providing a deep-learning framework for MOBIR. We evaluate our framework on several benchmark datasets. The framework has outperformed the state-of-the-art approaches, which uses deep models and deep representations to represent the recognition results. Moreover, our framework is particularly good at representing the different types of face, which is a difficult task for face recognition practitioners because of the complex face context information. We then apply the MOBIR framework to the context labeling as well as face classification and pose estimation tasks using deep neural networks (DNNs) in order to achieve state-of-the-art results on our benchmark dataset.
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