Lately, researchers in artificial intelligence have been really interested in
how language and vision come together, giving rise to the development of
multimodal models that aim to seamlessly integrate textual and visual
information. Multimodal models, an extension of Large Language Models (LLMs),
have exhibited remarkable capabilities in addressing a diverse array of tasks,
ranging from image captioning and visual question answering (VQA) to visual
grounding. While these models have showcased significant advancements,
challenges persist in accurately interpreting images and answering the
question, a common occurrence in real-world scenarios. This paper introduces a
novel approach to enhance the multimodal capabilities of existing models. In
response to the limitations observed in current Vision Language Models (VLMs)
and Multimodal Large Language Models (MLLMs), our proposed model Veagle,
incorporates a unique mechanism inspired by the successes and insights of
previous works. Veagle leverages a dynamic mechanism to project encoded visual
information directly into the language model. This dynamic approach allows for
a more nuanced understanding of intricate details present in visual contexts.
To validate the effectiveness of Veagle, we conduct comprehensive experiments
on benchmark datasets, emphasizing tasks such as visual question answering and
image understanding. Our results indicate a improvement of 5-6 \% in
performance, with Veagle outperforming existing models by a notable margin. The
outcomes underscore the model's versatility and applicability beyond
traditional benchmarks.

提出了一种名为 Veagle 的新方法，通过在现有模型中增强多模态能力，利用动态机制将编码的视觉信息直接投射到语言模型中，从而在视觉问题回答和图像理解等任务中表现出具有显著优势的 5-6% 的改进。