Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models

Published: 10 Jul 2024, Last Modified: 26 Aug 2024COLMEveryoneRevisionsBibTeXCC BY 4.0
Research Area: LMs on diverse modalities and novel applications
Keywords: Multimodal Large Language Model, Referring, Grounding
TL;DR: Ferret-v2, a significant upgrade to Ferret, which excels in detailed vision-related tasks without compromising their proficiency in global understanding.
Abstract: 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.
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Submission Number: 45
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