Documentos de Lluís Álvarez Gómez | REDI

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Área de conocimiento: "Aprendizaje automático"

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Conference Object(10)

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)(2)

Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022(2)

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR(2)

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning(1)

Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020(1)

Área temáticas

Funcionamiento de bibliotecas y archivos(4)

Métodos informáticos especiales(4)

Ciencias de la computación(2)

Medios documentales, educativos, informativos; periodismo(2)

Programación informática, programas, datos, seguridad(2)

Área de conocimiento

Ciencias de la computación(11)

Comunicación(2)

Análisis de datos(1)

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Palabras Claves

Document Analysis(2)

Multimodal retrieval(2)

Self-supervised learning(2)

Text embeddings(2)

Webly supervised learning(2)

Good news, everyone! context driven entity-aware captioning for news images

Conference Object

Abstract: Current image captioning systems perform at a merely descriptive level, essentially enumerating the

Palabras claves:

Document Analysis, Vision + Language

Furkan Biten A., Karatzas D., Lluís Álvarez Gómez, Rusiñol M.

Exploring hate speech detection in multimodal publications

Conference Object

Abstract: In this work we target the problem of hate speech detection in multimodal publications formed by a t

Palabras claves:

Gibert J., Gómez R., Karatzas D., Lluís Álvarez Gómez

Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval

Conference Object

Abstract: Scene text instances found in natural images carry explicit semantic information that can provide im

Palabras claves:

Dey S., Furkan Biten A., Karatzas D., Lluís Álvarez Gómez, Mafla A.

Object proposals for text extraction in the wild

Conference Object

Abstract: Object Proposals is a recent computer vision technique receiving increasing interest from the resear

Palabras claves:

Karatzas D., Lluís Álvarez Gómez

One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition

Conference Object

Abstract: Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data a

Palabras claves:

Document Analysis

Dey S., Fornes A., Furkan Biten A., Karatzas D., Kessentini Y., Llados J., Lluís Álvarez Gómez, Souibgui M.A.

Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting

Conference Object

Abstract: In this paper, we explore and evaluate the use of ranking-based objective functions for learning sim

Palabras claves:

Ranking loss, Smooth-AP, Smooth-nDCG, Word spotting

Llados J., Lluís Álvarez Gómez, Molina A., Ramos-Terrades O., Riba P.

Learning to learn from web data through deep semantic embeddings

Conference Object

Abstract: In this paper we propose to learn a multimodal image and text embedding from Web and Social Media da

Palabras claves:

Multimodal embeddings, Multimodal retrieval, Self-supervised learning, Text embeddings, Webly supervised learning

Gibert J., Gómez R., Karatzas D., Lluís Álvarez Gómez

Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching

Conference Object

Abstract: The task of image-text matching aims to map representations from different modalities into a common

Palabras claves:

Vision and Languages

Furkan Biten A., Karatzas D., Lluís Álvarez Gómez, Mafla A.

LSDE: Levenshtein Space Deep Embedding for Query-by-String Word Spotting

Conference Object

Abstract: In this paper we present the LSDE string representation and its application to handwritten word spot

Palabras claves:

CNNs, Deep embeddings, Handwritten Keyword Spotting, Query by string

Karatzas D., Lluís Álvarez Gómez, Rusiñol M.

Self-supervised learning from web data for multimodal retrieval

Abstract: Self-supervised learning from multimodal image and text data allows deep neural networks to learn po

Palabras claves:

Multimodal embedding, Multimodal retrieval, Self-supervised learning, Text embeddings, Webly supervised learning

Gibert J., Gómez R., Karatzas D., Lluís Álvarez Gómez