EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning
<|reference_start|>EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning: News image captioning requires model to generate an informative caption rich in entities, with the news image and the associated news article. Current MLLMs still bear limitations in handling entity information in news image captioning tasks. Besides, generating high-quality news image captions 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 EAMA: an Entity-Aware Multimodal Alignment based approach for News Image Captioning. Our approach first aligns the MLLM with two extra alignment tasks: Entity-Aware Sentence Selection task and Entity Selection task, together with News Image Captioning task. The aligned MLLM will utilize the additional entity-related information extracted by itself to supplement the textual input while generating news image captions. Our approach achieves better results than all previous models on two mainstream news image captioning datasets.<|reference_end|>
@article{zhang2024eama,
    title={EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image
  Captioning},
    author={Junzhe Zhang and Huixuan Zhang and Xunjian Yin and Xiaojun Wan},
    journal={arXiv preprint arXiv:2402.19404},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.19404},
    primaryClass={cs.CV cs.CL}
}