Open-Vocabulary Video Scene Graph Generation via Union-aware Semantic Alignment
Ziyue Wu, Junyu Gao, Changsheng Xu · 2024
Video Scene Graph Generation (VidSGG) plays a crucial role in various visual-language tasks by providing accessible structured visual relation knowledge. However, the requirement of annotating all categories of prevailing VidSGG methods limits their application in real-world scenarios. Despite the popular VLMs facilitating preliminary exploration of open-vocabulary VidSGG tasks, the correspondence between visual union regions and relation predicates is usually ignored. Therefore, we propose an Open-vocabulary VidSGG framework named Union-Aware Semantic Alignment Network (UASAN) to explore the alignment between visual union regions and relation predicate concepts in the same semantic space. Specifically, a visual refiner is designed to acquire open-vocabulary knowledge and the ability to bridge different modalities. To achieve better alignment, we first design a semantic-aware context encoder to achieve a comprehensive semantic interaction between object trajectories, visual union regions, and trajectory motion information to obtain semantic-aware union region representations. Then, a union-relation alignment decoder is utilized to generate the discriminative relation token for each union region for final relation prediction.