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Summary
The Relation-Aware Hierarchical Prompting (RAHP) framework is proposed to address the challenges of open-vocabulary scene graph generation (OV-SGG) by enhancing text representations. RAHP integrates entity-aware and region-aware relation text prompts, enabling more accurate and flexible image-text matching.
Highlights
- RAHP framework is proposed for OV-SGG task.
- Entity-aware and region-aware text prompts are integrated to enhance text representation.
- A dynamic selection mechanism is introduced to filter out irrelevant prompts.
- RAHP achieves state-of-the-art performance on Visual Genome and Open Images v6 datasets.
- RAHP demonstrates robustness across different LLMs and backbone models.
- The framework can be seamlessly extended to open-vocabulary scenarios.
Key Insights
- RAHP's entity-aware and region-aware text prompts provide a richer representation of relationships, leading to improved performance in OV-SGG tasks.
- The dynamic selection mechanism in RAHP effectively reduces noise from irrelevant prompts, enhancing the model's robustness.
- RAHP's ability to generalize to novel relationships is attributed to its open-vocabulary design, which enables the model to learn from a broader range of relationships.
- The framework's flexibility in accommodating different LLMs and backbone models makes it a versatile tool for various applications.
- RAHP's performance on both Visual Genome and Open Images v6 datasets demonstrates its effectiveness in handling diverse and complex visual scenarios.
- The qualitative results of RAHP-generated scene graphs highlight the framework's ability to identify novel relationships and provide a deeper understanding of visual scenes.
- The comparison with RECODE on the zero-shot visual relationship detection task showcases RAHP's superior performance in recognizing relationships with limited training data.
Mindmap
Citation
Liu, T., Li, R., Wang, C., & He, X. (2024). Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2412.19021