23 citations
- La Rochelle UniversitéFR7 papers
- Computer Vision Center1 paper
- Institut de Recherche en Informatique de ToulouseFR1 paper
- Laboratoire d'Informatique Gaspard-MongeFR1 paper
- National Research University Higher School of EconomicsRU1 paper
- RIKENJP1 paper
- Université du Québec en OutaouaisCA1 paper
- Université Gustave EiffelFR1 paper
- Université Toulouse III - Paul SabatierFR1 paper
10 papers
RAPTOR: Refined Approach for Product Table Object Recognition
Eliott Thomas, Mickael Coustaty, Aurelie Joseph +4
Extracting tables from documents is a critical task across various industries, especially on business documents like invoices and reports. Existing systems based on DEtection TRans…
Scalable Framework for Classifying AI-Generated Content Across Modalities
Anh-Kiet Duong, Petra Gomez-Krämer
The rapid growth of generative AI technologies has heightened the importance of effectively distinguishing between human and AI-generated content, as well as classifying outputs fr…
IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering
Musab Al-Ghadi, Joris Voerman, Souhail Bakkali +3
The increasing use of digital technologies and mobile-based registration procedures highlights the vital role of personal identity documents (IDs) in verifying users and safeguardi…
Next Priority Concept: A new and generic algorithm computing concepts from complex and heterogeneous data
Christophe Demko, Karell Bertet, Cyril Faucher +2
In this article, we present a new data type agnostic algorithm calculating a concept lattice from heterogeneous and complex data. Our NextPriorityConcept algorithm is first introdu…
Using musical relationships between chord labels in automatic chord extraction tasks
Tristan Carsault, Jérôme Nika, Philippe Esling
Recent researches on Automatic Chord Extraction (ACE) have focused on the improvement of models based on machine learning. However, most models still fail to take into account the…
Dynamic Multi-Task Learning for Face Recognition with Facial Expression
Zuheng Ming, Junshi Xia, Muhammad Muzzamil Luqman +2
Benefiting from the joint learning of the multiple tasks in the deep multi-task networks, many applications have shown the promising performance comparing to single-task learning.…