Year: 2024
Type: conference-paper
Abstract: This paper introduces Arabic Contrastive Language-Image Pre-training (AraCLIP), a model designed for Arabic image retrieval tasks, building upon the Contrastive Language-Image Pre-training (CLIP) architecture.Ar-aCLIP leverages Knowledge Distillation to transfer cross-modal knowledge from English to Arabic, enhancing its ability to understand Arabic text and retrieve relevant images.Unlike existing multilingual models, AraCLIP is uniquely positioned to understand the intricacies of the Arabic language, including specific terms, cultural nuances, and contextual constructs.By leveraging the CLIP architecture as our foundation, we introduce a novel approach that seamlessly integrates textual and visual modalities, enabling AraCLIP to effectively retrieve images based on Arabic textual queries.We offer an online demonstration allowing users to input Arabic prompts and compare AraCLIP's performance with state-ofthe-art multilingual models.We conduct comprehensive experiments to evaluate AraCLIP's performance across diverse datasets, including Arabic XTD-11, and Arabic Flicker 8k.Our results showcase AraCLIP's superiority in image retrieval accuracy, demonstrating its effectiveness in handling Arabic queries.AraCLIP represents a significant advancement in crosslingual image retrieval, offering promising applications in Arabic language processing and beyond.See project page 1 .
Institutions: University of Sharjah, Prince Sattam Bin Abdulaziz University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402671327