Summary of Llava-next-interleave: Tackling Multi-image, Video, and 3d in Large Multimodal Models, by Feng Li et al.
LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
by Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, Chunyuan Li
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed LLaVA-NeXT-Interleave model is a Large Multimodal Model that can tackle various multimodal scenarios, including multi-image, video, 3D, and single-image tasks. The model is designed to address the limitation of existing open LMMs, which primarily focus on single-image tasks. To achieve this, the authors introduce a new dataset called M4-Instruct, which consists of 1,177.6k samples spanning four primary domains with 14 tasks and 41 datasets. The authors also curate the LLaVA-Interleave Bench to comprehensively evaluate the multi-image performance of LMMs. Experimental results show that the proposed model achieves leading results in multi-image, video, and 3D benchmarks while maintaining the performance of single-image tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new type of computer model called LLaVA-NeXT-Interleave can handle many different types of data at once, like pictures, videos, and 3D images. This is important because most existing models are only good at handling one type of data at a time. To test this model, the researchers created a big dataset with over 1 million examples from four main areas with 14 tasks and 41 smaller datasets. They also created a special benchmark to see how well the model can handle different types of images. The results show that the new model is very good at handling many types of data at once. |
