Groma: Localized Visual Tokenization for Grounding Multimodal Large
  Language Models
Authors: name: Chuofan Ma, name: Yi Jiang, name: Jiannan Wu, name: Zehuan Yuan, name: Xiaojuan Qi

We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded
and fine-grained visual perception ability. Beyond holistic image
understanding, Groma is adept at region-level tasks such as region captioning
and visual grounding. Such capabilities are built upon a localized visual
tokenization mechanism, where an image input is decomposed into regions of
interest and subsequently encoded into region tokens. By integrating region
tokens into user instructions and model responses, we seamlessly enable Groma
to understand user-specified region inputs and ground its textual output to
images. Besides, to enhance the grounded chat ability of Groma, we curate a
visually grounded instruction dataset by leveraging the powerful GPT-4V and
visual prompting techniques. Compared with MLLMs that rely on the language
model or external module for localization, Groma consistently demonstrates
superior performances in standard referring and grounding benchmarks,
highlighting the advantages of embedding localization into image tokenization.
Project page: https://groma-mllm.github.io/.