8 papers
Learning Quantifiable Visual Explanations Without Ground-Truth
Amritpal Singh, Andrey Barsky, Mohamed Ali Souibgui +2
Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to evaluate due to the lack of g…
Reading in the Dark: Low-light Scene Text Recognition
Xuanshuo Fu, Lei Kang, Ernest Valveny +2
Accurate text recognition in low-light environments is essential for intelligent systems in applications ranging from autonomous vehicles to smart surveillance. However, challenges…
Preserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition
Lei Kang, Xuanshuo Fu, Lluis Gomez +3
Handwritten Text Recognition (HTR) is crucial for document digitization, but handwritten data can contain user-identifiable features, like unique writing styles, posing privacy ris…
Enhancing Document VQA Models via Retrieval-Augmented Generation
Eric López, Artemis Llabrés, Ernest Valveny
Document Visual Question Answering (Document VQA) must cope with documents that span dozens of pages, yet leading systems still concatenate every page or rely on very large vision-…
LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and Differential Diagnosis
Lei Kang, Xuanshuo Fu, Oriol Ramos Terrades +3
Medical document analysis plays a crucial role in extracting essential clinical insights from unstructured healthcare records, supporting critical tasks such as differential diagno…
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…