Scene Graph Generation (SGG) is fundamental to structured visual understanding, yet existing benchmarks focus mainly on daily-life images and overlook scientific experiment scenes with specialized instruments, task-specific experimental semantics, and dense, fine-grained physical relations. Building upon PhysScene, our previously introduced SGG dataset for physics experiment scenes, we further identify two key challenges that such scientific environments pose to existing SGG models: a pronounced long-tail relational predicate distribution and a substantial visual-textual semantic gap. To address these challenges, we propose the Cross-Modal Dual-Path Generator (CM-DPG), a model for robust open-vocabulary SGG. The model enhances object-level semantic representations through joint visual-textual encoding and improves relational reasoning using complementary visual and geometric cues. We also incorporate relation-aware pre-training, caption-derived pseudo-supervision, and adaptive weighting to support balanced learning across head and tail predicates. Extensive experiments on PhysScene and VG150 show that CM-DPG achieves competitive performance across multiple evaluation settings, with ablation studies validating the contribution of each component. The dataset and code are publicly available at https://github.com/ZMH-SDUST/CM-DPG.
Prerana Ramkumar, Nouhaila Innan, Muhammad Shafiquequant-ph cs.LG
Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail predicate imbalance. Classical SGG models frequently rely on dataset statistics, leading to biased predictions toward frequent relations rather than fine-grained semantic predicates. Although existing debiasing strategies improve mean recall, predicate classification in current frameworks still often depends on large classical decision modules with high parameter cost. 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) trained using weighted cross-entropy. To the best of our knowledge, this is among the first studies to evaluate a hybrid quantum architecture for scene graph predicate classification on Visual Genome 150. We study the effect of qubit count, encoding strategy, entangling structure, and circuit depth on relational prediction. The best 4-qubit QP-Head uses Amplitude Embedding and Strongly Entangling Layers to compress 4096-dimensional pair features into a 16-dimensional quantum-compatible representation, corresponding to a 256$\times$ reduction. It achieves an mR@100 of 57.25%, compared with 41.1% for the classical CFEN reference, while using only 96 trainable quantum parameters. Scaling to 8 qubits maintains strong long-tail performance, reaching an mR@100 of 55.38% with 384 quantum parameters, while the depth analysis shows a trade-off between expressibility and runtime overhead. These results suggest that compact hybrid quantum predicate heads can support parameter-efficient long-tail relational classification in complex visual reasoning tasks.