A ra CLIP : Cross-Lingual Learning for Effective A rabic Image Retrieval (2024)

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.

## introduction text-to-image retrieval models have been extensively studied in the literature, garnering attention due to their applicability and remarkable success across diverse domains such as medical imaging @xcite , automated image captioning @xcite , visual question answering (aggarwal and kale, 1 https://arabic-clip.github.io/arabic-clip 2020), and text-to-image generation @xcite , among others. however, the majority of proposed models focus on english texts, neglecting the vast number of arabic speakers worldwide. the arabic language poses unique challenges, including its richness in morphology, complex grammar, and diverse dialects @xcite . therefore, we aim to push the boundaries by building a dedicated text-to-image retrieval model that caters to the arabic language. several learning approaches have been employed for text-to-image integration, including contrastive learning and teacher learning approaches. contrastive learning has demonstrated a powerful learning approach to align text-image pairs. @xcite proposed an efficient method called contrastive language-image pretraining (clip), which demonstrated capability in learning useful representations of text and images. however, the predominant focus has been on english text. also, contrastive learning was found to be compositionally heavy as it was first introduced in @xcite . the cross-lingual approach for text-to-image synthesis has gained significant attention in recent years, particularly in the context of crosslingual models that can effectively capture the nuances and complexities of various languages. @xcite employed an italian model instead of a multi-lingual model, where cross-lingual models can capture the nuances and complexities of the italian language, leading to more accurate image-text retrieval results. the teacherlearning approach for text-image integration for multiple languages was introduced in @xcite as they trained cross-lingual models for the swedish language and multi-lingual models for many languages (about 68 languages) by utilizing machine translation while starting from a pretrained text encoder for the needed target language. to this end, this paper utilizes this technique to introduce the first arabic clip, a model trained using teacher learning to align arabic text with images. "araclip" is a substantial extension of the clip model @xcite specifically designed for image retrieval tasks in the arabic language. araclip is tailored to understand the unique terms and contextual constructs that form the arabic language. building upon the transformative clip architecture, we propose a novel approach that leverages the power of knowledge distillation @xcite to seamlessly transfer the cross-modal knowledge encoded in a pretrained english textual model to an arabic counterpart. our key contributions in this paper are as follows: ## methodology in our research, we employed the method of knowledge distillation (teacher learning), as described by @xcite which is a technique that has become increasingly popular in the 2 https://huggingface.co/datasets/khalidalt/ xtd_11 3 https://huggingface.co/spaces/arabic-clip/ araclip field of artificial intelligence @xcite . knowledge distillation is a process where a smaller model, known as the student, is trained to mimic the behavior of a larger, pre-trained model, known as the teacher. this approach is particularly useful when dealing with large models that are computationally expensive to train and fine-tune. by distilling the knowledge from the teacher model into the student model, we can achieve similar performance with reduced computational costs. there are many approaches to train clip models, such as contrastive learning and teacher learning for the text encoder. contrastive learning is a type of self-supervised learning that involves training a model to differentiate between similar and dissimilar inputs. in the image-text retrieval systems, contrastive learning can be used to train a model to learn a joint representation of images and texts by maximizing the similarity between matching pairs and minimizing the similarity between non-matching pairs. however, we found that contrastive learning is computationally expensive to train and fine-tune a clip model using it. therefore, we have chosen the teacher learning approach, which depends on fine-tuning the text encoder for the arabic language with a text encoder from an english clip model. in particular, we applied the teacher learning method by treating an english text model as the "teacher" and a pre-trained arabic model as the "student". this approach allows us to leverage the knowledge learned by the english model and adapt it to the arabic language, achieving better performance with reduced computational costs. by using knowledge distillation and teacher learning, we were able to develop an efficient and effective image-text retrieval system that can retrieve relevant images from a large database based on a given text query. our approach has shown promising results, and we believe it has the potential to be applied in various real-world applications, such as image search engines and multimedia retrieval systems. ## model training and experimental setup we conducted extensive experiments using the teacher-learning approach, building upon the code provided by @xcite and incorporating our own enhancements and integrations. the datasets used for training, validation, and evaluation are summarized in this table is organized into three sections, each corresponding to a different experimental setup. the first section presents the datasets processed by the vit-b-16-plus-240 model, where the english encoder served as the teacher model. we pre-computed the embeddings of the english captions to reduce computation time and memory requirements during training and validation. the datasets used in this setup were (cc3m+cc12m+sbu) and mscoco, with training samples from both datasets and validation samples only from (cc3m+cc12m+sbu). the second section details the datasets used when the english encoder of the vit-b-16-siglib-512 model was the teacher model. again, the training sets consisted of both (cc3m+cc12m+sbu) and mscoco datasets, while the validation set was only from (cc3m+cc12m+sbu). the third section lists the evaluation datasets, which comprised arabic_xtd-11 and ara-bic_flicker_8k. these datasets were used with various metrics, as discussed in section 4. by using these diverse datasets, we aimed to comprehensively evaluate the performance of our model across different problem domains and input types. during the training, the main goal was to maximize the cosine similarity between the embeddings of the english and arabic encoders using the teacher-learning approach. ## models evaluation evaluating the performance of our approach relative to existing methods is crucial for understanding its strengths and limitations. in this section, we present a comparison of our work to a stateof-the-art multi-lingual model, using both quantitative and qualitative evaluation metrics. quantitatively, we compare our approach to others using metrics such as mrr and recall, while qualitatively, we assess the model's understaning of the text queries based on two categories such as complex sentence category performance under noise category. our results show that our approach outperforms the existing multi-lingual model in several key tasks. for text-image retrieval systems, two crucial metrics are mean reciprocal rank (mrr) and recall. mrr measures the reciprocal of the rank at which the first relevant item appears in the search results, averaged over all queries. it emphasizes the importance of ranking relevant items high in the search results. recall, on the other hand, measures the proportion of relevant items that are retrieved out of all relevant items in the dataset. it is often calculated at a specific cutoff (e.g., recall@k), which means it only considers the top k retrieved items. both metrics are important for evaluating the performance of text-image retrieval our proposed models for text-image retrieval are designed to leverage the strengths of both arabic language understanding and visual representation learning. the models are, the first mdoel is arabert-v2-base-vit-b-16-siglip-512-2m: this model combines the arabert-v2-base language model with the vit-b-16-siglip-512 visual transformer, trained on 2 million samples. the second model is arabert-v2-base-vit-b-16-siglip-512-2m-mscoco: this model is similar to the previous one but fine-tuned on the mscoco dataset, which provides additional training data for the model to learn from. the last model is arabert-v2large-vit-b-16-plus-240-2m: this model uses the larger arabert-v2-large language model and the vit-b-16-plus-240 visual transformer, trained on 2 million samples.