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I2D-SGG: scene graph generation via joint modeling of intra- and inter-relationship dependencies
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DOI：10.1007/s40747-025-02208-w.png)
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Abstract
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Scene graph generation (SGG), which involves jointly detecting entities and inferring their relationships from images, plays a critical role in high-level visual scene understanding and reasoning tasks. Most existing SGG methods primarily focus on learning dependencies within individual triplets and follow a unidirectional reasoning paradigm, thereby overlooking the reverse constraints from predicates to entities. Moreover, they generally fail to capture inter-relationship dependencies, resulting in isolated predictions that ignore the global contextual information formed by shared entities or semantic associations. To address these limitations, this paper proposes I2D-SGG, a novel framework that jointly models both Intra- and Inter-relationship Dependencies to improve the accuracy and efficacy of SGG. First, we introduce a triple-decoder architecture with dedicated modules for decoding subject, object, and predicate, connected through a prior-enhanced sparse relation matrix. Second, decoupled conditional queries comprising position queries and content queries are strengthened via cross-layer fusion and bidirectional attention, facilitating deeper geometric and semantic interaction within each triplet. Third, a global correlation graph-based reasoning module is employed to model inter-relationships across triplets. This module utilizes Graph Convolutional Networks (GCNs) to enable cross-triplet message passing and dynamic feature aggregation, thereby supporting global context-aware relational reasoning beyond isolated triplet. Experiments on the VG-150 dataset demonstrate that I2D-SGG achieves a mean Recall@100 (mR@100) of 35.41%, outperforming the state-of-the-art one-stage method by 1.57%. Qualitative analyses further confirm its superior capability in fine-grained scene understanding. Ablation studies validate the effectiveness and generalizability of our proposed dual dependency modeling mechanism. I2D-SGG enhances the model's capacity to comprehend both intra- and inter-relationship, overcoming limitations of unidirectional propagation, entangled query design, and isolated triplet reasoning in conventional approaches, thereby offering a new perspective for fine-grained relational modeling in complex visual scenes.
Keywords:
Scene graph generation
Intra-relationship dependencies
Inter-relationship dependencies
Global correlation graph
Computational Intelligence
Complexity
Data Structures and Information Theory
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