AraCLIP: Cross-Lingual Learning for Effective Arabic Image Retrieval
Muhammad Al-Barham, Imad Afyouni, Khalid Almubarak, Ashraf Elnagar, Ayad Turky, Ibrahim Hashem
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. AraCLIP 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-of-the-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 cross-lingual image retrieval, offering promising applications in Arabic language processing and beyond.- Anthology ID:
- 2024.arabicnlp-1.9
- Volume:
- Proceedings of The Second Arabic Natural Language Processing Conference
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Nizar Habash, Houda Bouamor, Ramy Eskander, Nadi Tomeh, Ibrahim Abu Farha, Ahmed Abdelali, Samia Touileb, Injy Hamed, Yaser Onaizan, Bashar Alhafni, Wissam Antoun, Salam Khalifa, Hatem Haddad, Imed Zitouni, Badr AlKhamissi, Rawan Almatham, Khalil Mrini
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 102–110
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.9
- DOI:
- Cite (ACL):
- Muhammad Al-Barham, Imad Afyouni, Khalid Almubarak, Ashraf Elnagar, Ayad Turky, and Ibrahim Hashem. 2024. AraCLIP: Cross-Lingual Learning for Effective Arabic Image Retrieval. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 102–110, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- AraCLIP: Cross-Lingual Learning for Effective Arabic Image Retrieval (Al-Barham et al., ArabicNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.arabicnlp-1.9.pdf