activity
20182026
most citedTowards Speeding up Adversarial Training in Latent Spaces

1 citations · 1 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CL2026

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…

cs.CV2024

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…

cs.CV2021

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…

cs.LG20211 cited

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…

cs.LG2020

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…

cs.LG2020

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…