Multi-modal emotion recognition has recently gained a lot of attention since
it can leverage diverse and complementary relationships over multiple
modalities, such as audio, visual, and text. Most state-of-the-art methods for
multimodal fusion rely on recurrent networks or conventional attention
mechanisms that do not effectively leverage the complementary nature of the
modalities. In this paper, we focus on dimensional emotion recognition based on
the fusion of facial, vocal, and text modalities extracted from videos.
Specifically, we propose a recursive cross-modal attention (RCMA) to
effectively capture the complementary relationships across the modalities in a
recursive fashion. The proposed model is able to effectively capture the
inter-modal relationships by computing the cross-attention weights across the
individual modalities and the joint representation of the other two modalities.
To further improve the inter-modal relationships, the obtained attended
features of the individual modalities are again fed as input to the cross-modal
attention to refine the feature representations of the individual modalities.
In addition to that, we have used Temporal convolution networks (TCNs) to
capture the temporal modeling (intra-modal relationships) of the individual
modalities. By deploying the TCNs as well cross-modal attention in a recursive
fashion, we are able to effectively capture both intra- and inter-modal
relationships across the audio, visual, and text modalities. Experimental
results on validation-set videos from the AffWild2 dataset indicate that our
proposed fusion model is able to achieve significant improvement over the
baseline for the sixth challenge of Affective Behavior Analysis in-the-Wild
2024 (ABAW6) competition.

通过跨模态注意力和时间卷积网络，提出了一种用于多模态情感识别的模型，能够有效地捕捉不同模态之间的互补关系，并在赛事中实现显著改进。

情感维度识别中的递归跨模态注意力的多模态融合

Recursive Cross-Modal Attention for Multimodal Fusion in Dimensional  Emotion Recognition

This paper presents an approach for detecting unilateral arm
paralysis/weakness using kinematic data. Our method employs temporal
convolution networks and recurrent neural networks, guided by knowledge
distillation, where we use inertial measurement units attached to the body to
capture kinematic information such as acceleration, rotation, and flexion of
body joints during an action. This information is then analyzed to recognize
body actions and patterns. Our proposed network achieves a high paretic
detection accuracy of 97.99\%, with an action classification accuracy of
77.69\%, through knowledge sharing. Furthermore, by incorporating causal
reasoning, we can gain additional insights into the patient's condition, such
as their Fugl-Meyer assessment score or impairment level based on the machine
learning result. Overall, our approach demonstrates the potential of using
kinematic data and machine learning for detecting arm paralysis/weakness. The
results suggest that our method could be a useful tool for clinicians and
healthcare professionals working with patients with this condition.

使用运动数据和机器学习来检测单侧手臂麻痹 / 无力，提出了一种通过时间卷积网络和循环神经网络结合知识蒸馏的方法，利用身体上的惯性测量单元来捕捉动作过程中的运动信息，进而实现对动作和模式识别。我们的实验表明，该方法具有很高的麻痹检测准确度（97.99%）和动作分类准确度（77.69%），同时结合因果推理可获取患者的 Fugl-Meyer 评分和受损程度等附加信息，该方法有望成为临床医生和医疗专业人员检测这种病情的有效工具。

中风后肢体瘫痪的运动学数据检测

After-Stroke Arm Paresis Detection using Kinematic Data

Given an untrimmed video, repetitive actions counting aims to estimate the
number of repetitions of class-agnostic actions. To handle the various length
of videos and repetitive actions, also optimization challenges in end-to-end
video model training, down-sampling is commonly utilized in recent
state-of-the-art methods, leading to ignorance of several repetitive samples.
In this paper, we attempt to understand repetitive actions from a full temporal
resolution view, by combining offline feature extraction and temporal
convolution networks. The former step enables us to train repetition counting
network without down-sampling while preserving all repetition regardless of the
video length and action frequency, and the later network models all frames in a
flexible and dynamically expanding temporal receptive field to retrieve all
repetitions with a global aspect. We experimentally demonstrate that our method
achieves better or comparable performance in three public datasets, i.e.,
TransRAC, UCFRep and QUVA. We expect this work will encourage our community to
think about the importance of full temporal resolution.

本文提出结合离线特征提取和时间卷积网络从全面的时间分辨率角度理解重复动作，设计了不需要下采样即可保留所有重复内容的重复计数网络，并利用动态扩展的时间感受野框架来检索所有重复内容，实验证明该方法优于或可与其他方法相媲美。

全分辨率重复计数

Full Resolution Repetition Counting

A modern vehicle fitted with sensors, actuators, and Electronic Control Units
(ECUs) can be divided into several operational subsystems called Functional
Working Groups (FWGs). Examples of these FWGs include the engine system,
transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels
that gauge vehicular operating conditions. This data rich environment is
conducive to the development of Predictive Maintenance (PdM) technologies.
Undercutting various PdM technologies is the need for robust anomaly detection
models that can identify events or observations which deviate significantly
from the majority of the data and do not conform to a well defined notion of
normal vehicular operational behavior. In this paper, we introduce the Vehicle
Performance, Reliability, and Operations (VePRO) dataset and use it to create a
multi-phased approach to anomaly detection. Utilizing Temporal Convolution
Networks (TCN), our anomaly detection system can achieve 96% detection accuracy
and accurately predicts 91% of true anomalies. The performance of our anomaly
detection system improves when sensor channels from multiple FWGs are utilized.

本文介绍了基于 Temporal Convolution Networks 的多阶段异常检测方法，使用 Vehicle Performance，Reliability 和 Operations (VePRO) 数据集，结合多个 FWG 的传感器来提供高达 96% 的检测精度和 91% 的准确预测。