VidSGG Datasets

Video Scene Graph Generation (VidSGG) aims to create dynamic representations of video content by identifying objects and their relationships over time. Current research focuses on improving the accuracy and efficiency of VidSGG models, often employing transformer architectures and addressing challenges like biased data distributions and the need for robust temporal modeling. These advancements are significant for improving video understanding in various applications, including autonomous driving, video retrieval, and content analysis. The development of large-scale datasets and novel algorithms that incorporate spatial and temporal context are key areas of ongoing investigation.