Author

Jun-Jie Zhou

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Preprint Aug 2026

WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report

Universal multimodal embeddings are becoming a core component of modern AI systems, enabling heterogeneous content to be represented in a shared space for applications such as retrieval, recommendation, classification, and agentic systems. In this report, we present WeMM-Embedding, a family of universal multimodal embe...

Jun-Jie Zhou, Ke Mei, Lei Li et al. · 4 citations

Preprint Aug 2026

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding

This work introduces GeoMEB, a large-scale multimodal embedding benchmark that standardizes 45 urban evaluation tasks across retrieval, visual question answering, change detection, classification, and visual grounding, and presents Geo-Embed, a unified embedding model that adapts a shared vision-language backbone to in...

Jia-Peng Li, Yong Li, Jun-Jie Zhou et al. · 0 citations