# GSE: Evaluating Sticker Visual Semantic Similarity via a General Sticker Encoder

arXiv: https://arxiv.org/abs/2511.04977 · HTML: https://mlsanity.com/paper/2511.04977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [TGIF: A New Dataset and Benchmark on Animated GIF Description](https://mlsanity.com/paper/1604.02748/) (2016)
- [PMG : Personalized Multimodal Generation with Large Language Models](https://mlsanity.com/paper/2404.08677/) (2024)
- [Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog](https://mlsanity.com/paper/2003.04679/) (2020)
- [Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark](https://mlsanity.com/paper/2109.01839/) (2021)
- [DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition](https://mlsanity.com/paper/2504.17349/) (2025)
- [Sticker820K: Empowering Interactive Retrieval with Stickers](https://mlsanity.com/paper/2306.06870/) (2023)
- [VSD2M: A Large-scale Vision-language Sticker Dataset for Multi-frame Animated Sticker Generation](https://mlsanity.com/paper/2412.08259/) (2024)
- [MGHFT: Multi-Granularity Hierarchical Fusion Transformer for Cross-Modal Sticker Emotion Recognition](https://mlsanity.com/paper/2507.18929/) (2025)
- [Towards Real-World Stickers Use: A New Dataset for Multi-Tag Sticker Recognition](https://mlsanity.com/paper/2403.05428/) (2024)
- [PerSRV: Personalized Sticker Retrieval with Vision-Language Model](https://mlsanity.com/paper/2410.21801/) (2024)
- [Small Stickers, Big Meanings: A Multilingual Sticker Semantic Understanding Dataset with a Gamified Approach](https://mlsanity.com/paper/2506.01668/) (2025)

## Abstract (arXiv)

Stickers have become a popular form of visual communication, yet understanding their semantic relationships remains challenging due to their highly diverse and symbolic content. In this work, we formally {define the Sticker Semantic Similarity task} and introduce {Triple-S}, the first benchmark for this task, consisting of 905 human-annotated positive and negative sticker pairs. Through extensive evaluation, we show that existing pretrained vision and multimodal models struggle to capture nuanced sticker semantics. To address this, we propose the {General Sticker Encoder (GSE)}, a lightweight and versatile model that learns robust sticker embeddings using both Triple-S and additional datasets. GSE achieves superior performance on unseen stickers, and demonstrates strong results on downstream tasks such as emotion classification and sticker-to-sticker retrieval. By releasing both Triple-S and GSE, we provide standardized evaluation tools and robust embeddings, enabling future research in sticker understanding, retrieval, and multimodal content generation. The Triple-S benchmark and GSE have been publicly released and are available here.