Arabic_IC: Research on Multi-Model Arabic Image Caption Generation

The Arabic_IC project aims to fill the gap in image caption generation for low-resource languages like Arabic, and systematically evaluate the performance of mainstream large-scale generative models such as Google Gemini, Gemma, and Llama on this task. Based on the Flickr dataset, it explores the capability boundaries of modern vision-language models in generating high-quality, semantically rich, and linguistically coherent Arabic captions, with a focus on the development of AI technology for low-resource languages and the fairness of global accessibility.

Vision-Language Models (VLMs) have made significant progress in high-resource languages like English, but their support for low-resource languages such as Arabic is limited, ignoring the important status of Arabic as the mother tongue of hundreds of millions of people. Arabic image caption generation faces unique challenges: morphological complexity (multiple vocabulary derived from root words),特殊性 of the writing system (right-to-left writing, letter form changes), dialect diversity (need to clearly evaluate standard variants), and data scarcity (insufficient image-text aligned data).

Three models are selected for evaluation: Google Gemini (closed-source commercial model with strong multilingual and multimodal capabilities), Gemma (Google open-source model, reproducible and customizable), and Llama (representative of the open-source community, excellent performance in its visual version). Based on the standard Flickr dataset (containing daily scene images and reference captions), the quality of captions generated by the models is compared with reference captions. Evaluation metrics include: BLEU/METEOR (vocabulary overlap), semantic similarity (semantic matching evaluated by pre-trained models), and human evaluation (subjective dimensions such as fluency, accuracy, and completeness).

Experiments show that the closed-source Gemini model outperforms open-source models (reflecting the multilingual data advantage of commercial models); among open-source models, Gemma has good fluency (standard grammar), and Llama has high semantic accuracy (captures key content); the performance gain from increasing model size for low-resource languages is less obvious than for high-resource languages. Typical errors include inappropriate vocabulary selection (English loanwords), grammatical errors (morphological issues), and semantic deviations (content mismatch or omission).

Development paths for VLMs in low-resource languages: 1. Prioritize data quality (multimodal aligned data in the target language is key); 2. Cross-language transfer learning (transfer visual understanding capabilities from high-resource languages to low-resource languages); 3. Synthetic data generation (expand training data via machine translation); 4. Improve evaluation benchmarks (promote fair competition and progress).

Application value: Accessibility services (help visually impaired people understand images), content management (image search, classification, and recommendation), education (language learning and visual literacy). Social impact: Narrow the digital technology gap for Arabic, enhance fair opportunities for users and creators, and promote the democratization of AI technology (benefiting more language users worldwide).

The Arabic_IC project provides empirical data for Arabic visual language understanding, revealing the current technical status and room for improvement. Future outlook: More abundant multilingual training data, more efficient cross-language transfer methods, more完善 (perfect) evaluation benchmarks, and continuous improvement of image understanding capabilities for low-resource languages. It emphasizes that AI technology needs to pay attention to language diversity and realize inclusive value.