EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning

• URL: http://arxiv.org/abs/2402.19404v4
• Date: Mon, 6 May 2024 14:41:56 GMT
• Title: EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning
• Authors: Junzhe Zhang, Huixuan Zhang, Xunjian Yin, Xiaojun Wan,
• Abstract summary: News image captioning requires model to generate an informative caption rich in entities, with the news image and the associated news article. Current Multimodal Large Language Models (MLLMs) still bear limitations in handling entity information on news image captioning task. Our approach achieves better results than all previous models in CIDEr score on GoodNews dataset (72.33 -> 88.39) and NYTimes800k dataset (70.83 -> 85.61)
• Score: 55.033327333250455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: News image captioning requires model to generate an informative caption rich in entities, with the news image and the associated news article. Though Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in addressing various vision-language tasks, our research finds that current MLLMs still bear limitations in handling entity information on news image captioning task. Besides, while MLLMs have the ability to process long inputs, generating high-quality news image captions still requires a trade-off between sufficiency and conciseness of textual input information. To explore the potential of MLLMs and address problems we discovered, we propose : an Entity-Aware Multimodal Alignment based approach for news image captioning. Our approach first aligns the MLLM through Balance Training Strategy with two extra alignment tasks: Entity-Aware Sentence Selection task and Entity Selection task, together with News Image Captioning task, to enhance its capability in handling multimodal entity information. The aligned MLLM will utilizes the additional entity-related information it explicitly extracts to supplement its textual input while generating news image captions. Our approach achieves better results than all previous models in CIDEr score on GoodNews dataset (72.33 -> 88.39) and NYTimes800k dataset (70.83 -> 85.61).

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