Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models
<|reference_start|>Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models: While Ferret seamlessly integrates regional understanding into the Large Language Model (LLM) to facilitate its referring and grounding capability, it poses certain limitations: constrained by the pre-trained fixed visual encoder and failed to perform well on broader tasks. In this work, we unveil Ferret-v2, a significant upgrade to Ferret, with three key designs. (1) Any resolution grounding and referring: A flexible approach that effortlessly handles higher image resolution, improving the model's ability to process and understand images in greater detail. (2) Multi-granularity visual encoding: By integrating the additional DINOv2 encoder, the model learns better and diverse underlying contexts for global and fine-grained visual information. (3) A three-stage training paradigm: Besides image-caption alignment, an additional stage is proposed for high-resolution dense alignment before the final instruction tuning. Experiments show that Ferret-v2 provides substantial improvements over Ferret and other state-of-the-art methods, thanks to its high-resolution scaling and fine-grained visual processing.<|reference_end|>
@article{zhang2024ferret-v2:,
    title={Ferret-v2: An Improved Baseline for Referring and Grounding with Large
  Language Models},
    author={Haotian Zhang, Haoxuan You, Philipp Dufter, Bowen Zhang, Chen Chen,
  Hong-You Chen, Tsu-Jui Fu, William Yang Wang, Shih-Fu Chang, Zhe Gan, Yinfei
  Yang},
    journal={arXiv preprint arXiv:2404.07973},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07973},
    primaryClass={cs.CV}
}