Summary of Groma: Localized Visual Tokenization For Grounding Multimodal Large Language Models, by Chuofan Ma et al.
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
by Chuofan Ma, Yi Jiang, Jiannan Wu, Zehuan Yuan, Xiaojuan Qi
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Groma, a Multimodal Large Language Model (MLLM) that combines grounded and fine-grained visual perception with multimodal understanding. Unlike traditional MLLMs, which rely on external modules or language models for localization, Groma embeds region-level tasks such as captioning and grounding into its architecture. This allows Groma to understand user-specified region inputs and ground its textual output to images. The model’s 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. To evaluate Groma’s performance, the authors curate a visually grounded instruction dataset using GPT-4V and visual prompting techniques. The results demonstrate superior performances in standard referring and grounding benchmarks compared to MLLMs that rely on external modules or language models for localization. This highlights the advantages of embedding localization into image tokenization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Groma is a new type of AI model that can understand images and text together. It’s good at finding specific parts of an image and describing what it sees. This helps Groma have conversations about images, like what’s in a picture or what’s happening in it. To make this happen, the creators of Groma broke down images into smaller parts, or “regions,” and then taught the model to understand each region separately. They also created a special dataset with examples of how to use Groma correctly. When tested against other AI models that do similar things, Groma did better at understanding what people want it to say about an image. |
Keywords
» Artificial intelligence » Embedding » Gpt » Grounding » Large language model » Prompting » Tokenization
