Stacked Extraction and Characterization of Object Categories from Camera Residuals

Stacked Extraction and Characterization of Object Categories from Camera Residuals – This paper proposes an image recognition method that employs a hierarchical representation for categorization and segmentation in order to reduce the number of features for training and segmentation. We proposed a fully Convolutional neural network with a stacked architecture built specifically for classification and segmentation. The structure of this new architecture is described in terms of an evolutionary algorithm with no explicit feature selection or feature tracking. To validate the performance of the proposed method, a detailed analysis of the hierarchical representation with features from different cameras is presented. The proposed architecture can be viewed as an extension of the convolutional neural network architecture, as we show that it does not have any loss or loss with respect to a deep network. Using the new architecture our method performs a high accuracy classification task in ImageNet (7.2%) while performing at least 20.8% accuracy on the classification task as compared to the baseline.

In this paper, we propose an attention-based model for visual attention. Previous work explicitly uses the attention mechanism to learn attention maps instead of a feature. However, previous studies focused on the visual attention mechanism which was not explored. Here, we explore the visual attention mechanism using a feature. A key assumption in previous attention-based approaches is that visual attention consists of learning two representations of visual features, and each of these representations may be used in different tasks. We propose a novel visual attention mechanism that learns attention maps by visualizing the task at hand and using a deep learning algorithm to adaptively update the representations of visual features. Experimental results using a new state-of-the-art visual attention system, the CNN-D+R-DI, demonstrate that the proposed method achieves competitive recognition rate of 90.9 per cent (95%) on the MNIST dataset.

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Stacked Extraction and Characterization of Object Categories from Camera Residuals

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  • Towards a better understanding of the intrinsic value of training topic models

    PupilNet: Principled Face Alignment with Recurrent AttentionIn this paper, we propose an attention-based model for visual attention. Previous work explicitly uses the attention mechanism to learn attention maps instead of a feature. However, previous studies focused on the visual attention mechanism which was not explored. Here, we explore the visual attention mechanism using a feature. A key assumption in previous attention-based approaches is that visual attention consists of learning two representations of visual features, and each of these representations may be used in different tasks. We propose a novel visual attention mechanism that learns attention maps by visualizing the task at hand and using a deep learning algorithm to adaptively update the representations of visual features. Experimental results using a new state-of-the-art visual attention system, the CNN-D+R-DI, demonstrate that the proposed method achieves competitive recognition rate of 90.9 per cent (95%) on the MNIST dataset.


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