5 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…
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…
Prototype Augmented Hypernetworks for Continual Learning
Neil De La Fuente, Maria Pilligua, Daniel Vidal +4
Continual learning (CL) aims to learn a sequence of tasks without forgetting prior knowledge, but gradient updates for a new task often overwrite the weights learned earlier, causi…
DocVXQA: Context-Aware Visual Explanations for Document Question Answering
Mohamed Ali Souibgui, Changkyu Choi, Andrey Barsky +3
We propose DocVXQA, a novel framework for visually self-explainable document question answering. The framework is designed not only to produce accurate answers to questions but als…
One missing piece in Vision and Language: A Survey on Comics Understanding
Emanuele Vivoli, Mohamed Ali Souibgui, Andrey Barsky +3
Vision-language models have recently evolved into versatile systems capable of high performance across a range of tasks, such as document understanding, visual question answering,…