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 propose a new novel approach to the problem of learning natural dialogues via deep encoder-decoder neural networks (DCDNNs). First, we augment the deep convolutional network with two layers of DNNs: one for dialogue, and one for language. Next, we train the convolutional DCDNNs to learn a convolutional dictionary with various convolutional sub-problems. Our approach leverages both hand-crafted and annotated dialogues. We propose two models that outperform DCDNN-based models (both train the DCNNs to learn the dictionary). Finally, we experiment our approach on a large collection of 10,000 human and 100,000 character short dialogues. To evaluate our approach, we conduct a trial on an audience sample for the SemEval 2017 evaluation of a class of short dialogues with 2.2 million dialogues.

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Automatic Image Aesthetic Assessment Based on Deep Structured Attentions

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  • A Comprehensive Survey of Artificial Intelligence: Annotated Articles Database and its Tools and Resources

    Probabilistic Learning and Sparse Visual Saliency in Handwritten CharactersWe propose a new novel approach to the problem of learning natural dialogues via deep encoder-decoder neural networks (DCDNNs). First, we augment the deep convolutional network with two layers of DNNs: one for dialogue, and one for language. Next, we train the convolutional DCDNNs to learn a convolutional dictionary with various convolutional sub-problems. Our approach leverages both hand-crafted and annotated dialogues. We propose two models that outperform DCDNN-based models (both train the DCNNs to learn the dictionary). Finally, we experiment our approach on a large collection of 10,000 human and 100,000 character short dialogues. To evaluate our approach, we conduct a trial on an audience sample for the SemEval 2017 evaluation of a class of short dialogues with 2.2 million dialogues.


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