Towards Flexible Visual Relationship Segmentation
This work addresses the need for a cohesive framework in computer vision to handle multiple visual relationship tasks, representing an incremental advancement.
The paper tackles the problem of fragmented visual relationship understanding across tasks like human-object interaction detection, scene graph generation, and referring relationships by proposing FleVRS, a single model that integrates these aspects for segmentation, achieving improvements such as +1.9 mAP on HICO-DET and +11.4 Acc on VRD.
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.