RS-Net: Context-Aware Relation Scoring for Dynamic Scene Graph Generation

• URL: http://arxiv.org/abs/2511.08651v1
• Date: Thu, 13 Nov 2025 01:01:30 GMT
• Title: RS-Net: Context-Aware Relation Scoring for Dynamic Scene Graph Generation
• Authors: Hae-Won Jo, Yeong-Jun Cho,
• Abstract summary: Dynamic Scene Graph Generation (DSGG) models how object relations evolve over time in videos.<n>Existing methods are trained only on annotated object pairs and lack guidance for non-related pairs, making it difficult to identify meaningful relations during inference.<n>We propose Relation Scoring Network (RS-Net), a modular framework that scores the contextual importance of object pairs using both spatial interactions and long-range temporal context.
• Score: 1.7188280334580195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic Scene Graph Generation (DSGG) models how object relations evolve over time in videos. However, existing methods are trained only on annotated object pairs and lack guidance for non-related pairs, making it difficult to identify meaningful relations during inference. In this paper, we propose Relation Scoring Network (RS-Net), a modular framework that scores the contextual importance of object pairs using both spatial interactions and long-range temporal context. RS-Net consists of a spatial context encoder with learnable context tokens and a temporal encoder that aggregates video-level information. The resulting relation scores are integrated into a unified triplet scoring mechanism to enhance relation prediction. RS-Net can be easily integrated into existing DSGG models without architectural changes. Experiments on the Action Genome dataset show that RS-Net consistently improves both Recall and Precision across diverse baselines, with notable gains in mean Recall, highlighting its ability to address the long-tailed distribution of relations. Despite the increased number of parameters, RS-Net maintains competitive efficiency, achieving superior performance over state-of-the-art methods.

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