Summary of Hyvilm: Enhancing Fine-grained Recognition with a Hybrid Encoder For Vision-language Models, by Shiding Zhu et al.
HyViLM: Enhancing Fine-Grained Recognition with a Hybrid Encoder for Vision-Language Models
by Shiding Zhu, Wenhui Dong, Jun Song, Yingbo Wang, Yanan Guo, Bo Zheng
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces HyViLM, a multimodal large language model designed to process high-resolution images while retaining their context during encoding. The current approach of dynamically cropping images into smaller sub-images truncates objects and connected areas, causing semantic breaks. To address this limitation, the authors design a Hybrid Encoder that not only encodes individual sub-images but also interacts with detailed global visual features. They also propose an optimal feature fusion strategy for the dynamic cropping approach. Compared to state-of-the-art MLLMs under the same setting, HyViLM outperforms existing models in nine out of ten tasks, achieving a 9.6% improvement on the TextVQA task and a 6.9% enhancement on the DocVQA task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HyViLM is a new way for computers to understand pictures. Right now, big language models can only look at small parts of pictures, which makes them hard to understand. HyViLM lets computers process whole pictures while keeping their meaning. The authors designed a special way for computers to see and understand pictures called the Hybrid Encoder. This helps HyViLM be better than other computer vision models. |
Keywords
» Artificial intelligence » Encoder » Large language model
