Psychological studies have shown that Micro Gestures (MG) are closely linked
to human emotions. MG-based emotion understanding has attracted much attention
because it allows for emotion understanding through nonverbal body gestures
without relying on identity information (e.g., facial and electrocardiogram
data). Therefore, it is essential to recognize MG effectively for advanced
emotion understanding. However, existing Micro Gesture Recognition (MGR)
methods utilize only a single modality (e.g., RGB or skeleton) while
overlooking crucial textual information. In this letter, we propose a simple
but effective visual-text contrastive learning solution that utilizes text
information for MGR. In addition, instead of using handcrafted prompts for
visual-text contrastive learning, we propose a novel module called Adaptive
prompting to generate context-aware prompts. The experimental results show that
the proposed method achieves state-of-the-art performance on two public
datasets. Furthermore, based on an empirical study utilizing the results of MGR
for emotion understanding, we demonstrate that using the textual results of MGR
significantly improves performance by 6%+ compared to directly using video as
input.

基于微手势的情绪理解通过非言语身体手势实现情绪理解，与人类情绪密切相关，提出了一种简单而有效的视觉 - 文本对比学习解决方案，利用文本信息进行微手势识别，并通过实验证明，该方法在两个公共数据集上达到了最先进的性能，进一步证明了利用微手势的文本结果可显著提高情绪理解的性能。

通过上下文感知的视觉文本对比学习提升微手势情绪识别

Enhancing Micro Gesture Recognition for Emotion Understanding via  Context-aware Visual-Text Contrastive Learning

In real-world continual learning scenarios, tasks often exhibit intricate and
unpredictable semantic shifts, posing challenges for fixed prompt management
strategies. We identify the inadequacy of universal and specific prompting in
handling these dynamic shifts. Universal prompting is ineffective for tasks
with abrupt semantic changes, while specific prompting struggles with
overfitting under mild semantic shifts. To overcome these limitations, we
propose an adaptive prompting approach that tailors minimal yet sufficient
prompts based on the task semantics. Our methodology, SemPrompt, incorporates a
two-level semantic grouping process: macroscopic semantic assignment and
microscopic semantic refinement. This process ensures optimal prompt
utilization for varying task semantics, improving the efficiency and
effectiveness of learning in real-world CL settings. Our experimental results
demonstrate that SemPrompt consistently outperforms existing methods in
adapting to diverse semantic shifts in tasks.

我们提出了一种自适应提示方法，通过宏观语义分配和微观语义细化的过程，根据任务语义量身定制最小且足够的提示，以提高在真实连续学习环境中学习的效率和效果。实验证明，该方法在适应各种任务中的不同语义转变方面始终优于现有方法。

语义转移的一对多解决方案：持续学习的自适应提示调整

One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for  Continual Learning

In the field of natural language processing, sentiment analysis via deep
learning has a excellent performance by using large labeled datasets.
Meanwhile, labeled data are insufficient in many sentiment analysis, and
obtaining these data is time-consuming and laborious. Prompt learning devotes
to resolving the data deficiency by reformulating downstream tasks with the
help of prompt. In this way, the appropriate prompt is very important for the
performance of the model. This paper proposes an adaptive prompting(AP)
construction strategy using seq2seq-attention structure to acquire the semantic
information of the input sequence. Then dynamically construct adaptive prompt
which can not only improve the quality of the prompt, but also can effectively
generalize to other fields by pre-trained prompt which is constructed by
existing public labeled data. The experimental results on FewCLUE datasets
demonstrate that the proposed method AP can effectively construct appropriate
adaptive prompt regardless of the quality of hand-crafted prompt and outperform
the state-of-the-art baselines.

本文提出了一种自适应提示构建策略，利用 seq2seq-attention 结构获取输入序列的语义信息，并动态构建自适应提示，可以改善提示的质量，且通过预训练的提示有效地推广至其他领域。在 FewCLUE 数据集上的实验证明了该方法的有效性和超过先前最先进方法的表现。