AMCA-SGG: Adaptive multi-prototype learning and cross-modal alignment for unbiased scene graph generation

Abstract

Scene graph generation (SGG) aims to detect objects and recognize their semantic relationships from images, serving as an important bridge between visual perception and high-level semantic understanding. Existing debiasing methods primarily address inter-class long-tailed bias, while paying insufficient attention to intra-class semantic heterogeneity of predicates. High-frequency predicates typically encompass multiple semantic sub-patterns, causing instances of the same predicate to scatter widely in the visual feature space and resulting in a fundamental misalignment between feature distributions and classification objectives. To address these issues, we propose AMCA, an unbiased SGG framework integrating adaptive multi-prototype learning with cross-modal alignment. Specifically, the Multi-Prototype Construction (MPC) module employs a distribution-aware prototype allocation strategy to quantify the semantic distributional complexity of each predicate and dynamically assign prototype counts, explicitly modeling intra-class semantic heterogeneity. Building on this, the Hybrid Relation Refinement Module (HRRM) embeds semantic prototypes into the visual relation feature refinement process via a prototype-augmented attention mechanism, and further employs Mamba to model global contextual dependencies among different relation representations, achieving deep fusion of semantic priors and visual features. Furthermore, the Visual-Text Alignment (VTA) module aligns the fused visual relation representations with CLIP text embeddings, significantly enhancing the model’s ability to distinguish fine-grained semantics. Extensive experiments on Visual Genome and GQA demonstrate that AMCA achieves consistently competitive performance across multiple SGG tasks, with particularly strong improvements on the unbiased mR@K metric. Our code is publicly available at https://github.com/SJVision/AMCA.