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Semantic instance segmentation remains a challenge. We propose to tackle the problem with a discriminative loss function, operating at pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. Our approach of combining an offthe- shelf network with a principled loss function inspired...
In order to generate effective results, it is essential for a recommender system to model the information about the user interests (user profiles). A profile usually contains preferences that reflect the recommendation technique, so collaborative systems represent a user with the ratings given to items, while content-based approaches assign a score to semantic/text-based features of the evaluated...
Semantic textual similarity measures the semantic equivalence between a pair of sentences. Lexical overlapping approach evaluates similarity among a sentence pair depending on the number of terms the sentence pair shares. The similarity can be measured at same level of abstraction or at multi levels. This paper presents the influence of token similarity measures using lexical overlap semantic similarity...
Traditional text categorization methods only deal with the content of the documents and use some statistic based metrics to represent the documents. The representation is then used by a machine learning approach to determine the document class. In this picture, the meaning of the document is missing. In order to add meaning into the text categorization process, we start with using part-of-speech tagging...
Existing approaches for automatic image annotation usually suffer from two issues: (1) lacking a good quality distance metric for image semantic similarity measure; (2) rarely considering the correlation between labels assigned to each image. In this paper, we aim to resolve both of the problems simultaneously in a novel unified framework. Specifically, a proper distance metric is learned based on...
A case-based approach allows reuse without the usual and significant effort for making software explicitly reusable. We even support such reuse for only partially developed requirements, since it allows reuse already without the need to develop a “complete” specification first. The solution information (models and code) of (one of) the most similar problems can then be taken for reuse and adapted...
This work presents a semantic level no-reference image sharpness/blurriness metric under the guidance of top-down & bottom-up saliency map, which is learned based on eye-tracking data by SVM. Unlike existing metrics focused on measuring the blurriness in vision level, our metric more concerns about the image content and human's intention. We integrate visual features, center priority, and semantic...
The research on Information Extraction (IE) aims at providing more powerful information access tools to help people overcome the problem of information overloading. In this paper, the basic IE methods are summarized based on domain ontology. The ontology-driven IE model is elaborated in detail. Moreover, the features of ontology-driven IE are compared with those of document-driven IE. Then the performance...
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