This paper presents an efficient Multi-scale Transformer-based approach for
the task of Emotion recognition from Physiological data, which has gained
widespread attention in the research community due to the vast amount of
information that can be extracted from these signals using modern sensors and
machine learning techniques. Our approach involves applying a Multi-modal
technique combined with scaling data to establish the relationship between
internal body signals and human emotions. Additionally, we utilize Transformer
and Gaussian Transformation techniques to improve signal encoding effectiveness
and overall performance. Our model achieves decent results on the CASE dataset
of the EPiC competition, with an RMSE score of 1.45.

本文提出了一种高效的基于多尺度 Transformer 的方法，用于从生理数据中识别情感，并且利用多模态技术和缩放数据建立内部身体信号与人类情感之间的关系。同时使用高斯变换技术优化信号编码效果，并在 EPiC 比赛的 CASE 数据集上取得了 RMSE 得分 1.45 的好成绩。

基于多尺度 Transformer 网络的多生理信号情感识别

Multi-scale Transformer-based Network for Emotion Recognition from Multi  Physiological Signals

In fine-grained image recognition (FGIR), the localization and amplification
of region attention is an important factor, which has been explored a lot by
convolutional neural networks (CNNs) based approaches. The recently developed
vision transformer (ViT) has achieved promising results on computer vision
tasks. Compared with CNNs, Image sequentialization is a brand new manner.
However, ViT is limited in its receptive field size and thus lacks local
attention like CNNs due to the fixed size of its patches, and is unable to
generate multi-scale features to learn discriminative region attention. To
facilitate the learning of discriminative region attention without box/part
annotations, we use the strength of the attention weights to measure the
importance of the patch tokens corresponding to the raw images. We propose the
recurrent attention multi-scale transformer (RAMS-Trans), which uses the
transformer's self-attention to recursively learn discriminative region
attention in a multi-scale manner. Specifically, at the core of our approach
lies the dynamic patch proposal module (DPPM) guided region amplification to
complete the integration of multi-scale image patches. The DPPM starts with the
full-size image patches and iteratively scales up the region attention to
generate new patches from global to local by the intensity of the attention
weights generated at each scale as an indicator. Our approach requires only the
attention weights that come with ViT itself and can be easily trained
end-to-end. Extensive experiments demonstrate that RAMS-Trans performs better
than concurrent works, in addition to efficient CNN models, achieving
state-of-the-art results on three benchmark datasets.

通过使用注意力权重来测量原始图像对应的补丁符记的重要性，我们提出了递归注意力多尺度转换器 (RAMS-Trans)，它使用变压器的自我注意力以多尺度的方式递归地学习区分地区的注意力。

RAMS-Trans: 基于循环注意力多尺度变换器进行细粒度图像识别

RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained  Image Recognition

In this paper, we introduce the prior knowledge, multi-scale structure, into
self-attention modules. We propose a Multi-Scale Transformer which uses
multi-scale multi-head self-attention to capture features from different
scales. Based on the linguistic perspective and the analysis of pre-trained
Transformer (BERT) on a huge corpus, we further design a strategy to control
the scale distribution for each layer. Results of three different kinds of
tasks (21 datasets) show our Multi-Scale Transformer outperforms the standard
Transformer consistently and significantly on small and moderate size datasets.

该研究通过在自我注意模块中引入先前知识 - 多尺度结构，提出了一种 Multi-Scale Transformer，其使用多尺度多头自我注意来捕捉来自不同尺度的特征，通过对 21 个数据集进行的实验结果表明，相对于标准 Transformer，在小型和中等型数据集上，该多尺度变压器始终具有显着性能优势。