Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions. We hypothesize that capturing long-term temporal dependencies is the key to effective generation of dynamic scene graphs. We propose to learn the long-term dependencies in a video by capturing the object-level consistency and inter-object relationship dynamics over object-level long-term tracklets using transformers. Experimental results demonstrate that our Dynamic Scene Graph Detection Transformer (DSG-DETR) outperforms state-of-the-art methods by a significant margin on the benchmark dataset Action Genome. Our ablation studies validate the effectiveness of each component of the proposed approach. The source code is available at https://github.com/Shengyu-Feng/DSG-DETR.
@article{arxiv.2112.09828,
title = {Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs},
author = {Shengyu Feng and Subarna Tripathi and Hesham Mostafa and Marcel Nassar and Somdeb Majumdar},
journal= {arXiv preprint arXiv:2112.09828},
year = {2022}
}