6 papers
VLM-PAR: A Vision Language Model for Pedestrian Attribute Recognition
Abdellah Zakaria Sellam, Salah Eddine Bekhouche, Fadi Dornaika +2
Pedestrian Attribute Recognition (PAR) involves predicting fine-grained attributes such as clothing color, gender, and accessories from pedestrian imagery, yet is hindered by sever…
C-DiffDet+: Fusing Global Scene Context with Generative Denoising for High-Fidelity Car Damage Detection
Abdellah Zakaria Sellam, Ilyes Benaissa, Salah Eddine Bekhouche +3
Fine-grained object detection in challenging visual domains, such as vehicle damage assessment, presents a formidable challenge even for human experts to resolve reliably. While Di…
CVPD at QIAS 2025 Shared Task: An Efficient Encoder-Based Approach for Islamic Inheritance Reasoning
Salah Eddine Bekhouche, Abdellah Zakaria Sellam, Hichem Telli +2
Islamic inheritance law (Ilm al-Mawarith) requires precise identification of heirs and calculation of shares, which poses a challenge for AI. In this paper, we present a lightweigh…
Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for Culturally Diverse Art Style Classification
Abdellah Zakaria Sellam, Salah Eddine Bekhouche, Cosimo Distante +1
Art style classification remains a formidable challenge in computational aesthetics due to the scarcity of expertly labeled datasets and the intricate, often nonlinear interplay of…
LoLA-SpecViT: Local Attention SwiGLU Vision Transformer with LoRA for Hyperspectral Imaging
Fadi Abdeladhim Zidi, Djamel Eddine Boukhari, Abdellah Zakaria Sellam +4
Hyperspectral image classification remains a challenging task due to the high dimensionality of spectral data, significant inter-band redundancy, and the limited availability of an…
Mamba Adaptive Anomaly Transformer with association discrepancy for time series
Abdellah Zakaria Sellam, Ilyes Benaissa, Abdelmalik Taleb-Ahmed +2
Anomaly detection in time series is essential for industrial monitoring and environmental sensing, yet distinguishing anomalies from complex patterns remains challenging. Existing…