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pro vyhledávání: '"Almazán, Emilio"'
Textual noise, such as typos or abbreviations, is a well-known issue that penalizes vanilla Transformers for most downstream tasks. We show that this is also the case for sentence similarity, a fundamental task in multiple domains, e.g. matching, ret
Externí odkaz:
http://arxiv.org/abs/2307.02912
Product matching is a fundamental step for the global understanding of consumer behavior in e-commerce. In practice, product matching refers to the task of deciding if two product offers from different data sources (e.g. retailers) represent the same
Externí odkaz:
http://arxiv.org/abs/2207.02008
Traditional approaches to line segment detection typically involve perceptual grouping in the image domain and/or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a f
Externí odkaz:
http://arxiv.org/abs/2001.01788
Autor:
Arroyo, Roberto, Tovar, Javier, Delgado, Francisco J., Almazán, Emilio J., Serrador, Diego G., Hurtado, Antonio
In this work, we propose a new technique that combines appearance and text in a Convolutional Neural Network (CNN), with the aim of detecting regions of different textual categories. We define a novel visual representation of the semantic meaning of
Externí odkaz:
http://arxiv.org/abs/1905.10858
Autor:
Almazan, Emilio J.
The work presented in this thesis provides a framework for monitoring wide area indoor spaces built from multiple Microsoft Kinect sensors. A large field of coverage is achieved by placing the sensors in a non-overlapping configuration to reduce the
Externí odkaz:
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631351