Summary of Mplug-owl3: Towards Long Image-sequence Understanding in Multi-modal Large Language Models, by Jiabo Ye et al.
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models
by Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, Jingren Zhou
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This paper introduces a new multi-modal large language model called mPLUG-Owl3, which is designed to handle long image sequences in various scenarios such as retrieved image-text knowledge, interleaved image-text, and lengthy videos. The proposed architecture incorporates novel hyper attention blocks that integrate vision and language into a common semantic space, enabling the processing of extended multi-image scenarios. Experimental results show that mPLUG-Owl3 achieves state-of-the-art performance on single-image, multi-image, and video benchmarks, outperforming models with similar sizes. Additionally, the paper proposes a new evaluation metric called Distractor Resistance to assess the model’s ability to maintain focus amidst distractions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new type of language model that can understand long sequences of images, like videos or pictures from an album. The model is special because it combines information from both images and text to make decisions. This allows it to do tasks that require looking at multiple images in a row, like summarizing a video or recognizing patterns in a series of photos. |
Keywords
* Artificial intelligence * Attention * Language model * Large language model * Multi modal
