Visual Question Answering (VQA) has emerged as a highly engaging field in
recent years, attracting increasing research efforts aiming to enhance VQA
accuracy through the deployment of advanced models such as Transformers.
Despite this growing interest, there has been limited exploration into the
comparative analysis and impact of textual modalities within VQA, particularly
in terms of model complexity and its effect on performance. In this work, we
conduct a comprehensive comparison between complex textual models that leverage
long dependency mechanisms and simpler models focusing on local textual
features within a well-established VQA framework. Our findings reveal that
employing complex textual encoders is not invariably the optimal approach for
the VQA-v2 dataset. Motivated by this insight, we introduce an improved model,
ConvGRU, which incorporates convolutional layers to enhance the representation
of question text. Tested on the VQA-v2 dataset, ConvGRU achieves better
performance without substantially increasing parameter complexity.

本研究论文比较了复杂的文本模型和简单的局部文本特征模型在 VQA 中的效果，并发现采用复杂的文本编码器并不总是最优的选择。基于这一发现，论文提出了一种改进的模型 ConvGRU，通过引入卷积层增强了问题文本的表示，在 VQA-v2 数据集上实现了更好的性能而不显著增加参数复杂性。

增强的视觉问答：卷积的比较分析与文本特征提取

Enhanced Visual Question Answering: A Comparative Analysis and Textual  Feature Extraction Via Convolutions

Multimodal Emotion Recognition in Conversation (ERC) has garnered growing
attention from research communities in various fields. In this paper, we
propose a cross-modal fusion network with emotion-shift awareness (CFN-ESA) for
ERC. Extant approaches employ each modality equally without distinguishing the
amount of emotional information, rendering it hard to adequately extract
complementary and associative information from multimodal data. To cope with
this problem, in CFN-ESA, textual modalities are treated as the primary source
of emotional information, while visual and acoustic modalities are taken as the
secondary sources. Besides, most multimodal ERC models ignore emotion-shift
information and overfocus on contextual information, leading to the failure of
emotion recognition under emotion-shift scenario. We elaborate an emotion-shift
module to address this challenge. CFN-ESA mainly consists of the unimodal
encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM).
RUME is applied to extract conversation-level contextual emotional cues while
pulling together the data distributions between modalities; ACME is utilized to
perform multimodal interaction centered on textual modality; LESM is used to
model emotion shift and capture related information, thereby guide the learning
of the main task. Experimental results demonstrate that CFN-ESA can effectively
promote performance for ERC and remarkably outperform the state-of-the-art
models.

我们提出了一种带有情感转变意识的跨模态融合网络（CFN-ESA）用于对话中的多模态情感识别，结果显示 CFN-ESA 可以有效提高 ERC 的性能并显著优于现有模型。