title: Veagle: Advancements in Multimodal Representation Learning
summary: 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.
paper_authors._id: 65f3c0f513b997d812a1f5f5
paper_authors.name: Rajat Chawla
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-15T08:31:30.681Z
paper_authors._id: 655b8d65a8ec3f330f2089c8
paper_authors.avatarUrl: /avatars/228ab14333e61e99633f71d1fdbce749.svg
paper_authors.fullname: Rajat Chawla
paper_authors.type: user
paper_authors.user: rcrajat
paper_authors._id: 65f3c0f513b997d812a1f5f6
paper_authors.name: Arkajit Datta
paper_authors._id: 65f3c0f513b997d812a1f5f7
paper_authors.name: Tushar Verma
paper_authors._id: 65f3c0f513b997d812a1f5f8
paper_authors.name: Adarsh Jha
paper_authors._id: 65f3c0f513b997d812a1f5f9
paper_authors.name: Anmol Gautam
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-15T08:32:53.039Z
paper_authors._id: 6215bfa783e9e4dedb2e7f1d
paper_authors.avatarUrl: /avatars/6da19eac512faa1eae9f56f1f5650687.svg
paper_authors.fullname: Anmol Gautam
paper_authors.type: user
paper_authors.user: ag2428
paper_authors._id: 65f3c0f513b997d812a1f5fa
paper_authors.name: Ayush Vatsal
paper_authors._id: 65f3c0f513b997d812a1f5fb
paper_authors.name: Sukrit Chaterjee
paper_authors._id: 65f3c0f513b997d812a1f5fc
paper_authors.name: Mukunda NS
paper_authors._id: 65f3c0f513b997d812a1f5fd
paper_authors.name: Ishaan Bhola
paper_title: Veagle: Advancements in Multimodal Representation Learning
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) multimodal models image captioning visual question answering (VQA) visual grounding Vision Language Models (VLMs) Multimodal Large Language Models (MLLMs) Veagle dynamic mechanism encoded visual information language model visual prompts visual question answering image understanding FID (Fretchet Inception Distance) performance improvement benchmark datasets