Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment.
In this paper, we propose several methods to improve the performance of multiple object tracking (MOT), especially for humans, in dynamic environments such as robots and autonomous vehicles. The first method is to restore and re-detect unreliable results to improve the detection. The second is to restore noisy regions in the image before the tracking association to improve the identification. To implement the image restoration function used in these two methods, an image inference model based on SRGAN (super-resolution generative adversarial networks) is used. Finally, the third method includes an association method using face features to reduce failures in the tracking association. Three distance measurements are designed so that this method can be applied to various environments. In order to validate the effectiveness of our proposed methods, we select two baseline trackers for comparative experiments and construct a robotic environment that interacts with real people and provides services. Experimental results demonstrate that the proposed methods efficiently overcome dynamic situations and show favorable performance in general situations.
Zero-shot recognition (ZSR) aims to perform visual classification by category in the absence of training samples. The focus in most traditional ZSR models is using semantic knowledge about familiar categories to represent unfamiliar categories with only the visual appearance of an unseen object. In this research, we consider not only visual information but context to enhance the classifier’s cognitive ability in a multi-object scene. We propose a novel method, contextual inference, that uses external resources such as knowledge graphs and semantic embedding spaces to obtain similarity measures between an unseen object and its surrounding objects. Using the intuition that close contexts involve more related associations than distant ones, distance weighting is applied to each piece of surrounding information with a newly defined distance calculation formula. We integrated contextual inference into traditional ZSR models to calibrate their visual predictions, and performed extensive experiments on two different datasets for comparative evaluations. The experimental results demonstrate the effectiveness of our method through significant enhancements in performance.
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