Feature Learning for Image Search via Dynamic Contextual Policy Search


Feature Learning for Image Search via Dynamic Contextual Policy Search – Automating the localization of human-based models is one of the most challenging tasks among machine learning algorithms. For this work, we propose a novel, deep CNN-based framework for semantic object localization. Our CNN architecture achieves state-of-the-art performance in the semantic object tracking and object-level segmentation scenarios using a single frame of video. Experiments show that our framework significantly outperforms both state-of-the-art and fully-convolutional CNN models for various tasks without the need for a hand-crafted semantic model or hand-tuning of the model. We also achieve a 20x improvement in object tracking speed compared to our proposed framework by incorporating a fully convolutional neural network.

While a lot of work has been done on the concept of spatial attention from the human brain, little work has been done on the topic of attention-based retrieval. Instead, attention is typically employed by the brain to perform spatial learning, learning where information and contextual information are shared. However, most research on attention-based retrieval is done for the task of learning new visual features to replace the standard search in a single search. To improve the learning performance, researchers have focused mainly on deep learning methodologies for attention-based retrieval, but are not aware of the different task types. In this paper, we propose a new spatial attention method which is able to learn rich features from a multi-view and multi-view visual space, but to perform it on a single visual space, to be more efficient. We develop a learning task to learn spatial features for visual search by a hierarchical and multilingual recurrent neural network. Experiments on several standard datasets demonstrate the effectiveness of our method, compared to existing methods.

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Feature Learning for Image Search via Dynamic Contextual Policy Search

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  • Efficient Sparse Subspace Clustering via Matrix Completion

    Learning to Imitate Human Contextual Queries via Spatial Recurrent ModelWhile a lot of work has been done on the concept of spatial attention from the human brain, little work has been done on the topic of attention-based retrieval. Instead, attention is typically employed by the brain to perform spatial learning, learning where information and contextual information are shared. However, most research on attention-based retrieval is done for the task of learning new visual features to replace the standard search in a single search. To improve the learning performance, researchers have focused mainly on deep learning methodologies for attention-based retrieval, but are not aware of the different task types. In this paper, we propose a new spatial attention method which is able to learn rich features from a multi-view and multi-view visual space, but to perform it on a single visual space, to be more efficient. We develop a learning task to learn spatial features for visual search by a hierarchical and multilingual recurrent neural network. Experiments on several standard datasets demonstrate the effectiveness of our method, compared to existing methods.


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