Sticker820K: Empowering Interactive Retrieval with Stickers
Sijie Zhao, Yixiao Ge, Zhongang Qi, Lin Song, Xiaohan Ding, Zehua Xie,, Ying Shan

TL;DR
This paper introduces Sticker820K, a large-scale Chinese sticker dataset with rich annotations, and proposes StickerCLIP, a benchmark model that significantly improves sticker retrieval performance and explores prompt tuning to enhance large language models for sticker retrieval.
Contribution
The paper presents a new large-scale sticker dataset and a specialized benchmark model, StickerCLIP, tailored for sticker retrieval tasks, addressing the limitations of natural image models in emotive sticker data.
Findings
StickerCLIP outperforms CLIP with 66.0% higher mean recall on Sticker820K.
Prompt tuning enables LLMs to perform sticker retrieval through instructions without degrading task quality.
The dataset and models facilitate advanced analysis and retrieval of emotive stickers in communication.
Abstract
Stickers have become a ubiquitous part of modern-day communication, conveying complex emotions through visual imagery. To facilitate the development of more powerful algorithms for analyzing stickers, we propose a large-scale Chinese sticker dataset, namely Sticker820K, which consists of 820k image-text pairs. Each sticker has rich and high-quality textual annotations, including descriptions, optical characters, emotional labels, and style classifications. Although vision-language tasks in the domain of natural images have been well studied, directly applying the those models, such as CLIP, to sticker data is not an optimal solution due to the discrepant nature between natural and emotive image data. Therefore, we propose StickerCLIP as a benchmark model on the Sticker820K dataset. For the text-to-image retrieval task, our StickerCLIP demonstrates strong superiority over the CLIP, which…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Video Analysis and Summarization
MethodsContrastive Language-Image Pre-training
