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@@ -24,6 +24,5 @@ We observe that current *large multimodal models (LMMs)* still struggle with see
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![Teaser](https://github.com/MikeWangWZHL/VDLM/blob/main/figures/teaser.png?raw=true)
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To solve this challenge, we propose **Visually Descriptive Language Model (VDLM)**, a visual reasoning framework that operates with intermediate text-
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based visual descriptions—SVG representations and learned Primal Visual Description, which can be directly integrated into existing LLMs and LMMs. We demonstrate that VDLM outperforms state-of-the-art large multimodal models, such as GPT-4V, across various multimodal reasoning tasks involving vector graphics. See our [paper](https://arxiv.org/abs/2404.06479) for more details.
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![Overview](https://github.com/MikeWangWZHL/VDLM/blob/main/figures/overview.png?raw=true)
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![Teaser](https://github.com/MikeWangWZHL/VDLM/blob/main/figures/teaser.png?raw=true)
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To solve this challenge, we propose **Visually Descriptive Language Model (VDLM)**, a visual reasoning framework that operates with intermediate text-based visual descriptions—SVG representations and learned Primal Visual Description, which can be directly integrated into existing LLMs and LMMs. We demonstrate that VDLM outperforms state-of-the-art large multimodal models, such as GPT-4V, across various multimodal reasoning tasks involving vector graphics. See our [paper](https://arxiv.org/abs/2404.06479) for more details.
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![Overview](https://github.com/MikeWangWZHL/VDLM/blob/main/figures/overview.png?raw=true)
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