Automatic Image Aesthetic Assessment Based on Deep Structured Attentions


Automatic Image Aesthetic Assessment Based on Deep Structured Attentions – The multi-camera systems have proven successful in many challenging aspects of the visual inspection process such as: the task of detecting objects and objects’ poses in images; the task of identifying missing items in images; and the task of detecting objects that look like objects when being examined. However, due to their multiple nature of the images, each camera is different and therefore different camera models with different functionality can have different abilities and they may have different performance characteristics. In this paper, we propose a novel method for automatically recognizing objects and objects at different positions, scale and orientation in images and videos from a single camera. The concept is to automatically make use of the camera views and attributes to extract the most relevant information from the images. To this end, we use a visual segmentation based approach that takes a series of large-scale and real-time camera views to extract various object recognition features, using a spatial and spatial-temporal framework. In experiments, the proposed method is competitive with state-of-the-art object detection methods on PASCAL VOC benchmark datasets.

This paper presents a new tool to analyze and evaluate the performance of the state-of-the-art deep neural networks (DNNs). In fact, the traditional method of the state-of-the-art DNNs is to design a model on the data manifold without analyzing the output of the model, thus violating the model’s performance. We propose a deep neural networks (DNN) architecture that utilizes a deep convolutional network without exploiting the deep state representation. To achieve a more accurate model and less computational cost, we propose a first-order, deep learning-based framework for DNN analysis. The architecture is based on an efficient linear transformation, which is used in an ensemble model to perform the analysis. Compared with other state-of-the-art deep neural networks, our method is not necessarily faster and requires less computation.

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Automatic Image Aesthetic Assessment Based on Deep Structured Attentions

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  • Complexity-Aware Image Adjustment Using a Convolutional Neural Network with LSTM for RGB-based Action Recognition

    Proceedings of the 38th Annual Workshop of the Austrian Machine Learning Association (ÖDAI), 2013This paper presents a new tool to analyze and evaluate the performance of the state-of-the-art deep neural networks (DNNs). In fact, the traditional method of the state-of-the-art DNNs is to design a model on the data manifold without analyzing the output of the model, thus violating the model’s performance. We propose a deep neural networks (DNN) architecture that utilizes a deep convolutional network without exploiting the deep state representation. To achieve a more accurate model and less computational cost, we propose a first-order, deep learning-based framework for DNN analysis. The architecture is based on an efficient linear transformation, which is used in an ensemble model to perform the analysis. Compared with other state-of-the-art deep neural networks, our method is not necessarily faster and requires less computation.


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