Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs

Autor:	Feng, Shengyu, Tripathi, Subarna, Mostafa, Hesham, Nassar, Marcel, Majumdar, Somdeb
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: WACV 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.09828 Zobrazit plný text záznamu View this record from Arxiv