Summary of Omnifusion Technical Report, by Elizaveta Goncharova et al.
OmniFusion Technical Report
by Elizaveta Goncharova, Anton Razzhigaev, Matvey Mikhalchuk, Maxim Kurkin, Irina Abdullaeva, Matvey Skripkin, Ivan Oseledets, Denis Dimitrov, Andrey Kuznetsov
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed OmniFusion model combines a pre-trained large language model (LLM) with adapters for visual modality, allowing for better text and visual data coupling. The architecture is based on various design principles, including MLP and transformer adapters, CLIP ViT-based encoders, and different image encoding methods. The model is evaluated on 8 visual-language benchmarks, achieving the top score in various VQA tasks compared to open-source LLaVA-like solutions. The OmniFusion model also provides highly-detailed answers in different domains such as housekeeping, sightseeing, culture, medicine, handwritten equation recognition, and more. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The OmniFusion model is a new way for AI-based approaches to understand and work with both text and visual information. It uses a special kind of computer program called a large language model (LLM) and adds adapters that allow it to understand pictures too. The model was tested on many different tasks, like answering questions about pictures, and it did very well compared to other similar models. This means the OmniFusion model can be used in lots of different areas, such as helping with household chores, giving information about tourist attractions, or even recognizing handwritten math problems. |
Keywords
» Artificial intelligence » Large language model » Transformer » Vit
