Towards Flexible Visual Relationship Segmentation

Fangrui Zhu, Jianwei Yang, Huaizu Jiang

Keywords: Visual relationship segmentation, relationship understanding, human-object interaction, scene graph generation.

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.

Primary Area: Machine vision

Submission Number: 3507