This paper presents the results of the SUN team for the Compound Expressions
Recognition Challenge of the 6th ABAW Competition. We propose a novel
audio-visual method for compound expression recognition. Our method relies on
emotion recognition models that fuse modalities at the emotion probability
level, while decisions regarding the prediction of compound expressions are
based on predefined rules. Notably, our method does not use any training data
specific to the target task. The method is evaluated in multi-corpus training
and cross-corpus validation setups. Our findings from the challenge demonstrate
that the proposed method can potentially form a basis for development of
intelligent tools for annotating audio-visual data in the context of human's
basic and compound emotions. The source code is publicly available.

该论文提出了一种用于复合表情识别的新型视听方法，该方法基于融合情感概率的情感识别模型，并利用预定义规则进行复合表情的预测，结果表明该方法可作为开发标注音频 - 视觉数据中人类基本和复合情绪的智能工具的基础。

基于迟到模态融合和基于规则决策的音视化复合表达识别方法

Audio-Visual Compound Expression Recognition Method based on Late  Modality Fusion and Rule-based Decision

This paper considers the adaptation of the e-coaching concept at times of
emergencies and disasters, through aiding the e-coaching with intelligent tools
for monitoring humans' affective state. The states such as anxiety, panic,
avoidance, and stress, if properly detected, can be mitigated using the
e-coaching tactic and strategy. In this work, we focus on a stress monitoring
assistant tool developed on machine learning techniques. We provide the results
of an experimental study using the proposed method.

本研究考虑在紧急和灾难时期通过辅助情绪智能监测工具来改进电子辅导概念，以缓解焦虑、恐慌、回避和压力等状态，并关注机器学习技术开发的压力监测助手工具的实验研究结果。

智能电子教练的压力评估

Intelligent Stress Assessment for e-Coaching

Computer-Aided Design (CAD) applications are used in manufacturing to model
everything from coffee mugs to sports cars. These programs are complex and
require years of training and experience to master. A component of all CAD
models particularly difficult to make are the highly structured 2D sketches
that lie at the heart of every 3D construction. In this work, we propose a
machine learning model capable of automatically generating such sketches.
Through this, we pave the way for developing intelligent tools that would help
engineers create better designs with less effort. Our method is a combination
of a general-purpose language modeling technique alongside an off-the-shelf
data serialization protocol. We show that our approach has enough flexibility
to accommodate the complexity of the domain and performs well for both
unconditional synthesis and image-to-sketch translation.

本文介绍了一种机器学习模型，该模型能够自动生成 CAD 模型中最具挑战性的 2D 草图，并为开发可帮助工程师更轻松创建更好的设计的智能工具铺平了道路。该方法结合了通用语言模型技术和现成的数据序列化协议，适应了领域的复杂性，并在无条件合成和图像转换为草图方面表现出良好的性能。