  Scene Graph Generation (SGG) can extract abstract semantic relations between
entities in images as graph representations. This task holds strong promises
for other downstream tasks such as the embodied cognition of an autonomous
agent. However, to power such applications, SGG needs to solve the gap of
real-time latency. In this work, we propose to investigate the bottlenecks of
current approaches for real-time constraint applications. Then, we propose a
simple yet effective implementation of a real-time SGG approach using YOLOV8 as
an object detection backbone. Our implementation is the first to obtain more
than 48 FPS for the task with no loss of accuracy, successfully outperforming
any other lightweight approaches. Our code is freely available at
https://github.com/Maelic/SGG-Benchmark.
