@inproceedings{
jiang2024vrdone,
title={Vrd{ONE}: One-stage Video Visual Relation Detection},
author={Xinjie Jiang and Chenxi Zheng and Xuemiao Xu and Bangzhen Liu and Weiying Zheng and Huaidong Zhang and Shengfeng He},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=pjdvHKo2M0}
}
Video Visual Relation Detection (VidVRD) focuses on understanding how entities interact over time and space in videos, a key step for getting a deeper insight into video scenes beyond basic visual tasks. Traditional methods for VidVRD, challenged by its complexity, usually split the task into two parts: one for identifying what categories are present and another for figuring out their temporal boundaries. This split overlooks the natural connection between these elements. Addressing the need for recognizing entity independence and their interactions across a range of durations, we propose VrdONE, a streamlined yet efficacious one-stage model. VrdONE combines the features of subjects and objects, turning predicate detection into 1D instance segmentation on their combined representations. This setup allows for both category identification and binary mask generation in one go, eliminating the need for extra steps like proposal generation or post-processing. VrdONE facilitates the interaction of features across various frames, adeptly capturing both short-lived and enduring relations. Additionally, we introduce the Subject-Object Synergy (SOS) Module, enhancing how subjects and objects perceive each other before combining. VrdONE achieves state-of-the-art performances on both the VidOR benchmark and ImageNet-VidVRD, showcasing its superior capability in discerning relations across different temporal scales.
VrdONE: One-stage Video Visual Relation Detection
Xinjie Jiang Chenxi Zheng Xuemiao Xu Bangzhen Liu Weiying Zheng Huaidong Zhang Shengfeng He