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QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation. Use when analyzing quantum algorithms, complexity bounds, quantum ML architectures, or quantum error correction involving mathematical analysis and statistical methods.
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基于 SOC 职业分类
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| name | quantum-predicate-learning-sgg |
| description | QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation. Use when analyzing quantum algorithms, complexity bounds, quantum ML architectures, or quantum error correction involving mathematical analysis and statistical methods. |
| metadata | {"arxiv_id":"2606.04689","published":"2026-06-06","category":"quantum-ml"} |
Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is limited by severe long-tail predicate imbalance. This work introduces a hybrid quantum predicate classifier for SGG by replacing the classical predicate head in Causal Feature Enhancement Network (CFEN) with a Quantum Predicate Head (QP-Head). The best 4-qubit QP-Head uses Amplitude Embedding and Strongly Entangling Layers to compress 4096-dimensional pair features into 16-dimensional quantum-compatible representation (256x reduction), achieving mR@100 of 57.25% vs 41.1% classical CFEN reference with only 96 trainable quantum parameters. Demonstrates parameter-efficient long-tail relational classification in visual reasoning.
