VLRM: Vision-Language Models act as Reward Models for Image Captioning
<|reference_start|>VLRM: Vision-Language Models act as Reward Models for Image Captioning: In this work, we present an unsupervised method for enhancing an image captioning model (in our case, BLIP2) using reinforcement learning and vision-language models like CLIP and BLIP2-ITM as reward models. The RL-tuned model is able to generate longer and more comprehensive descriptions. Our model reaches impressive 0.90 R@1 CLIP Recall score on MS-COCO Carpathy Test Split.   Weights are available at https://huggingface.co/sashakunitsyn/vlrm-blip2-opt-2.7b.<|reference_end|>
@article{dzabraev2024vlrm:,
    title={VLRM: Vision-Language Models act as Reward Models for Image Captioning},
    author={Maksim Dzabraev, Alexander Kunitsyn, Andrei Ivaniuta},
    journal={arXiv preprint arXiv:2404.01911},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01911},
    primaryClass={cs.CV}
}