In real-world conversations, the diversity and ambiguity of stickers often lead to varied interpretations based on the context, necessitating the requirement for comprehensively understanding stickers and supporting multi-tagging. To address this challenge, we introduce StickerTAG, the first multi-tag sticker dataset comprising a collected tag set with 461 tags and 13,571 sticker-tag pairs, designed to provide a deeper understanding of stickers. Recognizing multiple tags for stickers becomes particularly challenging due to sticker tags usually are fine-grained attribute aware. Hence, we propose an Attentive Attribute-oriented Prompt Learning method, ie, Att$^2$PL, to capture informative features of stickers in a fine-grained manner to better differentiate tags. Specifically, we first apply an Attribute-oriented Description Generation (ADG) module to obtain the description for stickers from four attributes. Then, a Local Re-attention (LoR) module is designed to perceive the importance of local information. Finally, we use prompt learning to guide the recognition process and adopt confidence penalty optimization to penalize the confident output distribution. Extensive experiments show that our method achieves encouraging results for all commonly used metrics.

翻译：在现实对话中，贴纸的多样性和歧义性常导致基于上下文的不同解读，这要求对贴纸进行全面理解并支持多标签标注。为应对这一挑战，我们提出StickerTAG——首个包含461个标签和13,571个贴纸-标签对的多标签贴纸数据集，旨在深入理解贴纸内涵。由于贴纸标签通常具有细粒度属性感知特性，多标签识别尤为困难。为此，我们提出注意力导向的属性提示学习方法（Att$^2$PL），通过细粒度方式捕捉贴纸信息特征以更好区分标签。具体而言，首先应用属性导向描述生成模块（ADG）从四个维度获取贴纸描述，随后设计局部重注意力模块（LoR）感知局部信息的重要性，最终采用提示学习引导识别过程，并结合置信度惩罚优化抑制高置信度输出分布。大量实验表明，该方法在所有常用指标上均取得令人鼓舞的结果。