Recently, emotion recognition based on physiological signals has emerged as a
field with intensive research. The utilization of multi-modal, multi-channel
physiological signals has significantly improved the performance of emotion
recognition systems, due to their complementarity. However, effectively
integrating emotion-related semantic information from different modalities and
capturing inter-modal dependencies remains a challenging issue. Many existing
multimodal fusion methods ignore either token-to-token or channel-to-channel
correlations of multichannel signals from different modalities, which limits
the classification capability of the models to some extent. In this paper, we
propose a comprehensive perspective of multimodal fusion that integrates
channel-level and token-level cross-modal interactions. Specifically, we
introduce a unified cross attention module called Token-chAnnel COmpound (TACO)
Cross Attention to perform multimodal fusion, which simultaneously models
channel-level and token-level dependencies between modalities. Additionally, we
propose a 2D position encoding method to preserve information about the spatial
distribution of EEG signal channels, then we use two transformer encoders ahead
of the fusion module to capture long-term temporal dependencies from the EEG
signal and the peripheral physiological signal, respectively.
Subject-independent experiments on emotional dataset DEAP and Dreamer
demonstrate that the proposed model achieves state-of-the-art performance.

本研究提出一种综合的多模态融合方法，结合了基于渠道和记号的跨模态相互作用，同时使用两个变压器编码器来捕捉融合的长期时序依赖，从而在情感数据集 DEAP 和 Dreamer 上取得了最先进的性能。

TACOformer: 多模态情感识别的标记通道复合交叉注意力

TACOformer:Token-channel compounded Cross Attention for Multimodal  Emotion Recognition

We present a new table structure recognition (TSR) approach, called
TSRFormer, to robustly recognizing the structures of complex tables with
geometrical distortions from various table images. Unlike previous methods, we
formulate table separation line prediction as a line regression problem instead
of an image segmentation problem and propose a new two-stage DETR based
separator prediction approach, dubbed \textbf{Sep}arator \textbf{RE}gression
\textbf{TR}ansformer (SepRETR), to predict separation lines from table images
directly. To make the two-stage DETR framework work efficiently and effectively
for the separation line prediction task, we propose two improvements: 1) A
prior-enhanced matching strategy to solve the slow convergence issue of DETR;
2) A new cross attention module to sample features from a high-resolution
convolutional feature map directly so that high localization accuracy is
achieved with low computational cost. After separation line prediction, a
simple relation network based cell merging module is used to recover spanning
cells. With these new techniques, our TSRFormer achieves state-of-the-art
performance on several benchmark datasets, including SciTSR, PubTabNet and WTW.
Furthermore, we have validated the robustness of our approach to tables with
complex structures, borderless cells, large blank spaces, empty or spanning
cells as well as distorted or even curved shapes on a more challenging
real-world in-house dataset.

提出了一种名为 TSRFormer 的新的表格结构识别方法，使用线性回归问题而不是图像分割问题来预测表格的分隔线，并提出了新的两阶段 DETR 分隔线预测方法来直接从表图像中预测分隔线。使用新的技术后，我们的 TSRFormer 在几个基准数据集上都获得了最先进的表现。