OvDSGG: End-to-End Open-Vocabulary Dynamic Scene Graph Generation

• URL: http://arxiv.org/abs/2608.14835v1
• Date: Fri, 14 Aug 2026 19:12:40 GMT
• Title: OvDSGG: End-to-End Open-Vocabulary Dynamic Scene Graph Generation
• Abstract summary: We introduce OvDSGG, the first end-to-end framework for open-vocabulary DSGG.<n>We introduce a rigorous open-vocabulary DSGG benchmark adapted from Action Genome, with disjoint Base/subject splits for both objects and predicates.<n>OvDSGG significantly outperforms open-vocabulary baselines across all metrics, with zero-shot Recall@K$ scores higher than the next-best baseline.
• Score: 36.703819424111806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic scene graphs (DSGs) capture spatio-temporal interactions across videos as $\langle$subject, predicate, object$\rangle$ triplets, and underpin downstream tasks such as video captioning, video question answering, and action analysis. However, end-to-end dynamic scene graph generation (DSGG) methods are closed-set: they recognize only objects and predicates from a fixed training vocabulary and struggle with the long-tailed distribution of rare concepts, severely limiting their real-world applicability. Existing open-vocabulary models typically inherit pretrained large language models, resulting in multi-stage training and inference with substantial cost. We introduce OvDSGG, the first end-to-end framework for open-vocabulary DSGG. OvDSGG builds on top of an open-vocabulary Spatial Backbone and a Temporal Backbone; we further propose a Triplet Feature Extraction Module that bridges them, and a Visual-Language Alignment Module that preserves open-vocabulary recognition by learning an adaptive decision boundary in the joint visual-language feature space, without expensive knowledge distillation in existing methods. We further introduce a rigorous open-vocabulary DSGG benchmark adapted from Action Genome, with disjoint Base/Novel splits for both objects and predicates. OvDSGG significantly outperforms open-vocabulary baselines across all metrics, with zero-shot Recall@$K$ scores 10.0--20.4 percentage point higher than the next-best baseline, while on closed-set DSGG remaining competitive with state-of-the-art models. Code and benchmark are publicly available at https://github.com/jhelsby/OvDSGG/.

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