VLRM: Vision-Language Models act as Reward Models for Image Captioning

Maksim Dzabraev; Alexander Kunitsyn; Andrei Ivaniuta

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arXiv · 2404.01911

VLRM: Vision-Language Models act as Reward Models for Image Captioning

Maksim Dzabraev·Alexander Kunitsyn·Andrei Ivaniuta

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Abstract

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.

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BibTeX RIS

Maksim Dzabraev, Alexander Kunitsyn, Andrei Ivaniuta. 2024-04-02. VLRM: Vision-Language Models act as Reward Models for Image Captioning. https://arxiv.org/abs/2404.01911

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