Summary of Img-diff: Contrastive Data Synthesis For Multimodal Large Language Models, by Qirui Jiao et al.
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models
by Qirui Jiao, Daoyuan Chen, Yilun Huang, Bolin Ding, Yaliang Li, Ying Shen
First submitted to arxiv on: 8 Aug 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 A novel approach is introduced to advance fine-grained image recognition within Multimodal Large Language Models (MLLMs). The method, inspired by contrastive learning and image difference captioning, generates high-quality datasets for training MLLMs. The process involves generating pairs of similar images that emphasize object variations, pinpointing object differences using a Difference Area Generator, and articulating these differences with a Difference Captions Generator. This results in the “object replacement” dataset Img-Diff, which can be scaled as needed due to its automated nature. State-of-the-art MLLMs are fine-tuned on this generated dataset, achieving substantial improvements across various image difference and Visual Question Answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is developed to help computers better understand images. The method uses fake data that shows how objects in similar pictures can be different. This helps train special computer models called Multimodal Large Language Models (MLLMs). These models can then recognize tiny details in images, like what’s changed between two similar photos. By using this new way of making fake data, the MLLMs became much better at recognizing these differences. |
Keywords
» Artificial intelligence » Question answering
