4 citations · 5 across the 9 of their papers we have counts for
11 papers
Efficient Document Tampering Localization with Multi-Level Discrepancy Features and Unified DCT-Quantization Embedding
Mohamed Dhouib, Ye Zhu, Sonia Vanier +1
Localizing document tampering is extremely challenging, as manipulations are crafted to appear visually consistent and often leave only subtle traces that are nearly invisible to t…
Leveraging Contrastive Learning for a Similarity-Guided Tampered Document Data Generation Pipeline
Mohamed Dhouib, Davide Buscaldi, Sonia Vanier +1
Detecting tampered text in document images is a challenging task due to data scarcity. To address this, previous work has attempted to generate tampered documents using rule-based…
MUCH: A Multilingual Claim Hallucination Benchmark
Jérémie Dentan, Alexi Canesse, Davide Buscaldi +2
Claim-level Uncertainty Quantification (UQ) is a promising approach to mitigate the lack of reliability in Large Language Models (LLMs). We introduce MUCH, the first claim-level UQ…
PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models
Mohamed Dhouib, Davide Buscaldi, Sonia Vanier +1
Visual Language Models require substantial computational resources for inference due to the additional input tokens needed to represent visual information. However, these visual to…
Predicting memorization within Large Language Models fine-tuned for classification
Jérémie Dentan, Davide Buscaldi, Aymen Shabou +1
Large Language Models have received significant attention due to their abilities to solve a wide range of complex tasks. However these models memorize a significant proportion of t…
Reconstructing training data from document understanding models
Jérémie Dentan, Arnaud Paran, Aymen Shabou
Document understanding models are increasingly employed by companies to supplant humans in processing sensitive documents, such as invoices, tax notices, or even ID cards. However,…
Evaluating Adversarial Robustness on Document Image Classification
Timothée Fronteau, Arnaud Paran, Aymen Shabou
Adversarial attacks and defenses have gained increasing interest on computer vision systems in recent years, but as of today, most investigations are limited to images. However, ma…
DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents
Mohamed Dhouib, Ghassen Bettaieb, Aymen Shabou
Information Extraction from visually rich documents is a challenging task that has gained a lot of attention in recent years due to its importance in several document-control based…
EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System
Sofian Chaybouti, Achraf Saghe, Aymen Shabou
State-of-the-art extractive question-answering models achieve superhuman performances on the SQuAD benchmark. Yet, they are unreasonably heavy and need expensive GPU computing to a…
MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering
Sofian Chaybouti, Achraf Saghe, Aymen Shabou
This paper introduces MIX, a multi-task deep learning approach to solve open-ended question-answering. First, we design our system as a multi-stage pipeline of 3 building blocks: a…
VisualWordGrid: Information Extraction From Scanned Documents Using A Multimodal Approach
Mohamed Kerroumi, Othmane Sayem, Aymen Shabou
We introduce a novel approach for scanned document representation to perform field extraction. It allows the simultaneous encoding of the textual, visual and layout information in…