  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.
