A Hybrid Approach for 2D Face Retrieval


A Hybrid Approach for 2D Face Retrieval – The recent study showed that a face retrieval method using visual and phonological features is superior to visual ones in terms of computational efficiency and retrieval performance. However, many of the features used in such retrieval are not well suited for large scale face retrieval. In this paper, we propose a visual feature retrieval method based on deep neural network (DNN) learning to extract features from the face images for face retrieval. Specifically, we employ a deep model trained on two different features of the face images. After learning from this model, we extract features from images of the faces, which are then used for feature extraction and retrieval. Experimental results on the large-scale face retrieval dataset indicate that our proposed algorithm can achieve better retrieval performance for both visual and phonological features compared to the state-of-the-art face retrieval process.

This paper presents a novel method for the representation of information in the visual system. We propose a new hierarchical reinforcement learning (HRL) method that jointly learns from different contexts of visual data. The method, which is based on the idea of a novel hierarchical reinforcement learning (HRL) task, jointly learns and leverages the visual system to estimate the relevant information through various visual modalities, like RGBD. The presented method has been proven in practice, in some cases to be able to effectively learn different visual modalities. The proposed hierarchical reinforcement learning (HRL) method is implemented using the visual system and its visual input, which can be trained as a supervised learning algorithm. Experimental results show that the proposed HRL method outperforms existing methods for both challenging visual modalities and a variety of other visual modalities.

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A Hybrid Approach for 2D Face Retrieval

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    GANs: Training, Analyzing and Parsing Generative ModelsThis paper presents a novel method for the representation of information in the visual system. We propose a new hierarchical reinforcement learning (HRL) method that jointly learns from different contexts of visual data. The method, which is based on the idea of a novel hierarchical reinforcement learning (HRL) task, jointly learns and leverages the visual system to estimate the relevant information through various visual modalities, like RGBD. The presented method has been proven in practice, in some cases to be able to effectively learn different visual modalities. The proposed hierarchical reinforcement learning (HRL) method is implemented using the visual system and its visual input, which can be trained as a supervised learning algorithm. Experimental results show that the proposed HRL method outperforms existing methods for both challenging visual modalities and a variety of other visual modalities.


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