Search Results for author: Lluís Padró

Found 7 papers, 5 papers with code

Visual Semantic Relatedness Dataset for Image Captioning

1 code implementation20 Jan 2023 Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró

Modern image captioning system relies heavily on extracting knowledge from images to capture the concept of a static story.

Image Captioning text similarity

Zipf's laws of meaning in Catalan

no code implementations30 Jun 2021 Neus Català, Jaume Baixeries, Ramon Ferrer-Cancho, Lluís Padró, Antoni Hernández-Fernández

In his pioneering research, G. K. Zipf formulated a couple of statistical laws on the relationship between the frequency of a word with its number of meanings: the law of meaning distribution, relating the frequency of a word and its frequency rank, and the meaning-frequency law, relating the frequency of a word with its number of meanings.

Textual Visual Semantic Dataset for Text Spotting

no code implementations21 Apr 2020 Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró

In this paper, we propose a visual context dataset for Text Spotting in the wild, where the publicly available dataset COCO-text [Veit et al. 2016] has been extended with information about the scene (such as objects and places appearing in the image) to enable researchers to include semantic relations between texts and scene in their Text Spotting systems, and to offer a common framework for such approaches.

text similarity Text Spotting

Visual Re-ranking with Natural Language Understanding for Text Spotting

3 code implementations29 Oct 2018 Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró

We propose a post-processing approach to improve scene text recognition accuracy by using occurrence probabilities of words (unigram language model), and the semantic correlation between scene and text.

Language Modelling Natural Language Understanding +3

Visual Semantic Re-ranker for Text Spotting

1 code implementation23 Oct 2018 Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró

In this paper, we propose a post-processing approach to improve the accuracy of text spotting by using the semantic relation between the text and the scene.

Text Spotting

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