Summary of Qwen2-vl: Enhancing Vision-language Model’s Perception Of the World at Any Resolution, by Peng Wang et al.
Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution
by Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, Junyang Lin
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models, redefines the conventional predetermined-resolution approach in visual processing. The Naive Dynamic Resolution mechanism enables the model to dynamically process images of varying resolutions into different numbers of visual tokens, generating more efficient and accurate visual representations that align with human perceptual processes. The model also integrates Multimodal Rotary Position Embedding (M-RoPE), facilitating the effective fusion of positional information across text, images, and videos. By scaling both the model size and training data, the Qwen2-VL Series achieves highly competitive performance on various multimodal benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Qwen2-VL Series is a new way to process pictures and videos that makes it more efficient and accurate. It uses something called Naive Dynamic Resolution, which lets it look at images of different sizes in a special way. This helps the model understand pictures better, like humans do. The model also has something called Multimodal Rotary Position Embedding, which helps combine information from text, pictures, and videos. The paper shows that this new way of processing works well on lots of different tasks. |
Keywords
* Artificial intelligence * Embedding
