R-CNN: A Generative Model for Recommendation


R-CNN: A Generative Model for Recommendation – The most common framework for visual object tracking has been the deep learning-based approach based on the recent success of deep learning-based object segmentation schemes. Recently, a convolutional neural network (CNN) has been proposed to solve this problem of tracking objects. However, current models with deep architecture suffer from high variance and hence suffer from high computational complexity. In this paper, we propose a two-phase CNN to solve the tracking problem in the first phase. In the first phase, a CNN is designed to track the object along the path with an adaptive temporal model which is trained with the spatial-temporal relation between object categories. In the second phase, a CNN is trained to track the object along the path with a global temporal model. We evaluate the proposed CNN using a large state-of-the-art image segmentation dataset, and demonstrate the superiority of the proposed CNN over state-of-the-art approaches on real-world object tracking.

As the Web continues to evolve and evolve in unprecedented ways, and as people consume and interact with images and videos each day, the Web has become a powerful tool for the analysis of social interactions. We aim and conduct a real-time visual search for a common visual pattern of images and videos, and perform this search with a knowledge of what information in these images and videos are shared with each other using the web. We compare some approaches and show that visual search can be used to find related visual patterns, and present preliminary results to evaluate visual search techniques such as visual similarity and similarity discovery.

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R-CNN: A Generative Model for Recommendation

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  • A Data-Driven Approach to Generalization and Retrieval of Scientific Papers

    Learning to Find and Recommend Similarities Across Images and VideosAs the Web continues to evolve and evolve in unprecedented ways, and as people consume and interact with images and videos each day, the Web has become a powerful tool for the analysis of social interactions. We aim and conduct a real-time visual search for a common visual pattern of images and videos, and perform this search with a knowledge of what information in these images and videos are shared with each other using the web. We compare some approaches and show that visual search can be used to find related visual patterns, and present preliminary results to evaluate visual search techniques such as visual similarity and similarity discovery.


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