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
this https URL

我们提出了一种使用 YOLOV8 作为目标检测骨干网络的实时场景图生成方法，该方法在任务中首次实现超过 48 FPS 的速度，且没有准确度损失，成功胜过任何其他轻量级方法。

实时场景图生成

Real-Time Scene Graph Generation

Multiple object tracking is a critical task in autonomous driving. Existing
works primarily focus on the heuristic design of neural networks to obtain high
accuracy. As tracking accuracy improves, however, neural networks become
increasingly complex, posing challenges for their practical application in real
driving scenarios due to the high level of latency. In this paper, we explore
the use of the neural architecture search (NAS) methods to search for efficient
architectures for tracking, aiming for low real-time latency while maintaining
relatively high accuracy. Another challenge for object tracking is the
unreliability of a single sensor, therefore, we propose a multi-modal framework
to improve the robustness. Experiments demonstrate that our algorithm can run
on edge devices within lower latency constraints, thus greatly reducing the
computational requirements for multi-modal object tracking while keeping lower
latency.

多目标跟踪是自动驾驶中的关键任务。本文采用神经架构搜索（NAS）方法寻找低实时延迟、相对高准确度的高效跟踪架构。同时，提出多模态框架以提高鲁棒性，并通过实验证明算法可以在边缘设备上运行，从而大大降低多模态跟踪的计算需求和延迟。