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WeChat releases WeMM-Embedding multimodal models, achieving SOTA performance

By PulseAugur Editorial · [3 sources] ·

WeChat has introduced WeMM-Embedding, a new family of universal multimodal embedding models designed to represent text, images, videos, and interleaved multimodal inputs in a shared space. The models, available in 2B, 4B, and 9B variants, were trained in two stages and have demonstrated state-of-the-art performance on public benchmarks, with the 2B variant outperforming an 8B baseline on MMEB-v2. WeMM-Embedding has been deployed across various WeChat applications, including Channels, Official Accounts, and Moments, showing significant gains in recommendation and search functionalities. AI

IMPACT Sets new SOTA on multimodal benchmarks and enhances retrieval/recommendation capabilities within WeChat applications.

RANK_REASON The cluster describes a technical report detailing a new family of multimodal embedding models with benchmark results and release information.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

WeChat releases WeMM-Embedding multimodal models, achieving SOTA performance

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The cluster describes a technical report detailing a new family of multimodal embedding models with benchmark results and release information.
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COVERAGE [3]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jing Lyu ·

    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-Embeddin…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report

    WeMM-Embedding is a family of universal multimodal embedding models that align text, images, videos, and interleaved inputs in a shared space, achieving state-of-the-art retrieval and recommendation performance across public benchmarks and large-scale WeChat applications.

  3. arXiv cs.CV TIER_1 English(EN) · Junjie Zhou, Ke Mei, Lei Li, Tianyi Wang, Fengyun Rao, Jing Lyu ·

    WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report

    arXiv:2608.24053v1 Announce Type: new Abstract: 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 s…

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