Summary of Relation-aware Hierarchical Prompt For Open-vocabulary Scene Graph Generation, by Tao Liu et al.
Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation
by Tao Liu, Rongjie Li, Chongyu Wang, Xuming He
First submitted to arxiv on: 26 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 approach to Scene Graph Generation (SGG) is proposed, which overcomes limitations by aligning visual relationship representations with textual representations. This allows for the identification of novel relationships in real-world scenarios. However, existing methods are constrained by fixed text representations, limiting diversity and accuracy. To address this, a Relation-Aware Hierarchical Prompting (RAHP) framework is introduced, which integrates subject-object and region-specific relation information into text representation. The approach uses entity clustering to reduce complexity and generates detailed prompts using a large language model (LLM). A dynamic selection mechanism within Vision-Language Models (VLMs) also selects relevant prompts based on visual content, reducing noise. Experimental results on the Visual Genome and Open Images v6 datasets demonstrate state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand pictures is developed. This approach can find relationships in images that haven’t been seen before. However, current methods have limitations because they use fixed text representations. To solve this problem, a new framework called Relation-Aware Hierarchical Prompting (RAHP) is created. It uses information about the relationships between objects and regions to improve text representation. The approach also reduces complexity by grouping similar things together and generates detailed prompts using a large language model. This allows for more accurate understanding of images. |
Keywords
» Artificial intelligence » Clustering » Large language model » Prompting
