Summary of Armada: Attribute-based Multimodal Data Augmentation, by Xiaomeng Jin et al.
ARMADA: Attribute-Based Multimodal Data Augmentation
by Xiaomeng Jin, Jeonghwan Kim, Yu Zhou, Kuan-Hao Huang, Te-Lin Wu, Nanyun Peng, Heng Ji
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this paper, researchers tackle a crucial issue in Multimodal Language Models (MLMs): the high cost of annotating high-quality image-text pair data for fine-tuning and alignment. The existing multimodal data augmentation frameworks propose ways to augment image-text pairs but suffer from semantic inconsistency or generate unrealistic images. To address these issues, the authors introduce Attribute-based Multimodal Data Augmentation (ARMADA), a novel method that manipulates visual attributes of mentioned entities using knowledge-guided approaches. ARMADA extracts entities and their visual attributes from text data, searches for alternative values in knowledge bases and large language models, and utilizes an image-editing model to edit images with the extracted attributes. The framework generates semantically consistent yet distinctive image-text pairs, visually similar images of disparate categories, and modulates auxiliary visual attributes using commonsense knowledge. Empirical results over four downstream tasks demonstrate the efficacy of ARMADA in producing high-quality data and enhancing model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make Multimodal Language Models (MLMs) more effective by reducing the cost and improving the quality of training data. Existing solutions have limitations, such as generating unrealistic images or failing to match semantic meaning between text and images. To solve this problem, scientists developed a new method called ARMADA that uses knowledge-based approaches to generate high-quality image-text pairs. This approach can help improve the performance of MLMs in various applications. |
Keywords
» Artificial intelligence » Alignment » Data augmentation » Fine tuning
