Summary of Consistency Of Compositional Generalization Across Multiple Levels, by Chuanhao Li et al.
Consistency of Compositional Generalization across Multiple Levels
by Chuanhao Li, Zhen Li, Chenchen Jing, Xiaomeng Fan, Wenbo Ye, Yuwei Wu, Yunde Jia
First submitted to arxiv on: 18 Dec 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 compositional generalization approach is proposed to simultaneously generalize across multiple levels of novel compositions. The existing methods achieve promising results, but the consistency across different levels remains unexplored. To address this, a meta-learning based framework is introduced to progressively learn compositions from simple to complex for consistency. A GQA-CCG dataset is built and experimental results on visual question answering and temporal video grounding demonstrate the effectiveness of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way for AI models to understand combinations of things they’ve seen before, like sentences or phrases. The goal is to make sure the model can apply what it’s learned to new situations that are similar but not exactly the same. To do this, the researchers developed a new approach using “meta-learning” and divided their training data into smaller sets based on how complex the compositions were. They tested their approach on two tasks: answering questions about images and understanding video footage. The results show that their approach is effective. |
Keywords
» Artificial intelligence » Generalization » Grounding » Meta learning » Question answering
