Towards Flexible Visual Relationship Segmentation

• URL: http://arxiv.org/abs/2408.08305v1
• Date: Thu, 15 Aug 2024 17:57:38 GMT
• Title: Towards Flexible Visual Relationship Segmentation
• Authors: Fangrui Zhu, Jianwei Yang, Huaizu Jiang,
• Abstract summary: Visual relationship understanding has been studied separately in human-object interaction detection, scene graph generation, and relationships referring tasks. We propose FleVRS, a single model that seamlessly integrates the above three aspects in standard and promptable visual relationship segmentation. Our framework outperforms existing models in standard, promptable, and open-vocabulary tasks.
• Score: 25.890273232954055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual relationship understanding has been studied separately in human-object interaction(HOI) detection, scene graph generation(SGG), and referring relationships(RR) tasks. Given the complexity and interconnectedness of these tasks, it is crucial to have a flexible framework that can effectively address these tasks in a cohesive manner. In this work, we propose FleVRS, a single model that seamlessly integrates the above three aspects in standard and promptable visual relationship segmentation, and further possesses the capability for open-vocabulary segmentation to adapt to novel scenarios. FleVRS leverages the synergy between text and image modalities, to ground various types of relationships from images and use textual features from vision-language models to visual conceptual understanding. Empirical validation across various datasets demonstrates that our framework outperforms existing models in standard, promptable, and open-vocabulary tasks, e.g., +1.9 $mAP$ on HICO-DET, +11.4 $Acc$ on VRD, +4.7 $mAP$ on unseen HICO-DET. Our FleVRS represents a significant step towards a more intuitive, comprehensive, and scalable understanding of visual relationships.

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