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.

We solve large-scale regression problems for which the data are represented by a set of linear functions in a non-convex way. By using nonconvex functions, we also can approximate the sparsity problem. A practical algorithm to approximate a polynomial function is presented. The algorithm is proved to be significantly faster; it is shown to be efficient in practice.

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Automatic Image Aesthetic Assessment Based on Deep Structured Attentions

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    A Note on the SP Inference for Large-scale Covariate RegressionWe solve large-scale regression problems for which the data are represented by a set of linear functions in a non-convex way. By using nonconvex functions, we also can approximate the sparsity problem. A practical algorithm to approximate a polynomial function is presented. The algorithm is proved to be significantly faster; it is shown to be efficient in practice.


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