16 citations · 26 across the 9 of their papers we have counts for
15 papers
Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features
Nicola Messina, Giuseppe Amato, Fabrizio Falchi +2
Cross-modal retrieval is an important functionality in modern search engines, as it increases the user experience by allowing queries and retrieved objects to pertain to different…
Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders
Nicola Messina, Giuseppe Amato, Andrea Esuli +3
Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the task of cross-m…
Tuning Ranking in Co-occurrence Networks with General Biased Exchange-based Diffusion on Hyper-bag-graphs
Xavier Ouvrard, Jean-Marie Le Goff, Stéphane Marchand-Maillet
Co-occurence networks can be adequately modeled by hyper-bag-graphs (hb-graphs for short). A hb-graph is a family of multisets having same universe, called the vertex set. An effic…
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
Xavier Ouvrard, Jean-Marie Le Goff, Stéphane Marchand-Maillet
Traditional verbatim browsers give back information in a linear way according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need…
Learning by stochastic serializations
Pablo Strasser, Stephane Armand, Stephane Marchand-Maillet +1
Complex structures are typical in machine learning. Tailoring learning algorithms for every structure requires an effort that may be saved by defining a generic learning procedure…
Extracting localized information from a Twitter corpus for flood prevention
Etienne Brangbour, Pierrick Bruneau, Stéphane Marchand-Maillet +4
In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives. From the spatial persp…