Summary of Veagle: Advancements in Multimodal Representation Learning, by Rajat Chawla et al.
Veagle: Advancements in Multimodal Representation Learning
by Rajat Chawla, Arkajit Datta, Tushar Verma, Adarsh Jha, Anmol Gautam, Ayush Vatsal, Sukrit Chaterjee, Mukunda NS, Ishaan Bhola
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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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 The proposed model, Veagle, aims to enhance the multimodal capabilities of existing Vision Language Models (VLMs) and Multimodal Large Language Models (MLLMs). By incorporating a dynamic mechanism inspired by previous works, Veagle projects encoded visual information directly into the language model, allowing for a more nuanced understanding of intricate details. The paper conducts comprehensive experiments on benchmark datasets, emphasizing tasks such as visual question answering and image understanding. Results show a 5-6% improvement in performance, with Veagle outperforming existing models by a notable margin. This demonstrates the model’s versatility and applicability beyond traditional benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Veagle is a new way to help computers understand pictures better. Right now, computers are good at looking at pictures and answering questions about what they see. But sometimes, these computers have trouble understanding complex details in pictures. Veagle fixes this by allowing the computer to directly use information from the picture when trying to answer questions or describe what it sees. The creators of Veagle tested their model on several tasks and found that it performed better than other models by a small but significant amount. |
Keywords
» Artificial intelligence » Language model » Question answering
