Emerging with the support of computing and communications technologies,
Metaverse is expected to bring users unprecedented service experiences.
However, the increase in the number of Metaverse users places a heavy demand on
network resources, especially for Metaverse services that are based on
graphical extended reality and require rendering a plethora of virtual objects.
To make efficient use of network resources and improve the
Quality-of-Experience (QoE), we design an attention-aware network resource
allocation scheme to achieve customized Metaverse services. The aim is to
allocate more network resources to virtual objects in which users are more
interested. We first discuss several key techniques related to Metaverse
services, including QoE analysis, eye-tracking, and remote rendering. We then
review existing datasets and propose the user-object-attention level (UOAL)
dataset that contains the ground truth attention of 30 users to 96 objects in
1,000 images. A tutorial on how to use UOAL is presented. With the help of
UOAL, we propose an attention-aware network resource allocation algorithm that
has two steps, i.e., attention prediction and QoE maximization. Specially, we
provide an overview of the designs of two types of attention prediction
methods, i.e., interest-aware and time-aware prediction. By using the predicted
user-object-attention values, network resources such as the rendering capacity
of edge devices can be allocated optimally to maximize the QoE. Finally, we
propose promising research directions related to Metaverse services.

设计了一种基于用户兴趣的网络资源分配方案，以提高用户体验质量，并提出了关于 Metaverse 服务的有前途的研究方向。

探索面向注意力的网络资源配置用于定制的元宇宙服务

Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services

In recent years, predicting driver's focus of attention has been a very
active area of research in the autonomous driving community. Unfortunately,
existing state-of-the-art techniques achieve this by relying only on human gaze
information, thereby ignoring scene semantics. We propose a novel Semantics
Augmented GazE (SAGE) detection approach that captures driving specific
contextual information, in addition to the raw gaze. Such a combined attention
mechanism serves as a powerful tool to focus on the relevant regions in an
image frame in order to make driving both safe and efficient. Using this, we
design a complete saliency prediction framework - SAGE-Net, which modifies the
initial prediction from SAGE by taking into account vital aspects such as
distance to objects (depth), ego vehicle speed, and pedestrian crossing intent.
Exhaustive experiments conducted through four popular saliency algorithms show
that on $\mathbf{49/56\text{ }(87.5\%)}$ cases - considering both the overall
dataset and crucial driving scenarios, SAGE outperforms existing techniques
without any additional computational overhead during the training process. The
augmented dataset along with the relevant code are available as part of the
supplementary material.

该论文提出了一种基于场景语义和注视信息相结合的自动驾驶注意力预测方法 SAGE，并通过修改自动驾驶的关键预测参数，设计了一个完整的预测框架 SAGE-Net，实验证明 SAGE 在不增加计算量的情况下，可以优于现有技术 87.5% 的案例。

瞄准正确的物品：自动驾驶的引导性语义凝视

Looking at the right stuff: Guided semantic-gaze for autonomous driving

In this work we aim to predict the driver's focus of attention. The goal is
to estimate what a person would pay attention to while driving, and which part
of the scene around the vehicle is more critical for the task. To this end we
propose a new computer vision model based on a multi-branch deep architecture
that integrates three sources of information: raw video, motion and scene
semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes
for which eye-tracking annotations are available. This dataset features more
than 500,000 registered frames, matching ego-centric views (from glasses worn
by drivers) and car-centric views (from roof-mounted camera), further enriched
by other sensors measurements. Results highlight that several attention
patterns are shared across drivers and can be reproduced to some extent. The
indication of which elements in the scene are likely to capture the driver's
attention may benefit several applications in the context of human-vehicle
interaction and driver attention analysis.

本文旨在预测驾驶员的注意力集中情况，提出了一种基于多分支深度架构的计算机视觉模型，集成了三种信息源：原始视频、动态和场景语义，并介绍了 DR (eye) VE，这是迄今为止最大的驾驶场景数据集，结果强调了跨驾驶员共享的注意力模式，可在人车交互和驾驶员注意力分析等多个应用方面受益。