11 citations · 26 across the 12 of their papers we have counts for
12 papers
Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
Wassim Swaileh, Florent Imbert, Yann Soullard +2
Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction. In this work, we focus…
CVPD at QIAS 2026: RAG-Guided LLM Reasoning for Al-Mawarith Share Computation and Heir Allocation
Wassim Swaileh, Mohammed-En-Nadhir Zighem, Hichem Telli +4
Islamic inheritance (Ilm al-Mawarith) is a multi-stage legal reasoning task requiring the identification of eligible heirs, resolution of blocking rules (hajb), assignment of fixed…
ReWind: Understanding Long Videos with Instructed Learnable Memory
Anxhelo Diko, Tinghuai Wang, Wassim Swaileh +2
Vision-Language Models (VLMs) are crucial for applications requiring integrated understanding textual and visual information. However, existing VLMs struggle with long videos due t…
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training
Xiaoyu Liang, Yaguan Qian, Jianchang Huang +4
Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep lea…
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Yaguan Qian, Yuqi Wang, Bin Wang +3
Recent studies show deep neural networks (DNNs) are extremely vulnerable to the elaborately designed adversarial examples. Adversarial learning with those adversarial examples has…
Versailles-FP dataset: Wall Detection in Ancient
Wassim Swaileh, Dimitrios Kotzinos, Suman Ghosh +3
Access to historical monuments' floor plans over a time period is necessary to understand the architectural evolution and history. Such knowledge bases also helps to rebuild the hi…
Towards Speeding up Adversarial Training in Latent Spaces
Yaguan Qian, Qiqi Shao, Tengteng Yao +5
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples. However, existing adversarial training methods consume unbearable…
TEAM: We Need More Powerful Adversarial Examples for DNNs
Yaguan Qian, Ximin Zhang, Bin Wang +4
Although deep neural networks (DNNs) have achieved success in many application fields, it is still vulnerable to imperceptible adversarial examples that can lead to misclassificati…
Towards a Neural Model for Serial Order in Frontal Cortex: a Brain Theory from Memory Development to Higher-Level Cognition
Alexandre Pitti, Mathias Quoy, Catherine Lavandier +3
In order to keep trace of information and grow up, the infant brain has to resolve the problem about where old information is located and how to index new ones. We propose that the…
TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples
Ya-guan Qian, Xi-Ming Zhang, Wassim Swaileh +5
Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples.Adversarial training is one of the most effect…
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units
Wassim Swaileh, Yann Soullard, Thierry Paquet
We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language…
A syllable based model for handwriting recognition
Wassim Swaileh, Thierry Paquet
In this paper, we introduce a new modeling approach of texts for handwriting recognition based on syllables. We propose a supervised syllabification approach for the French and Eng…