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Omni-Embed-Mini integrates multiple modalities without text quality loss

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Omni-Embed-Mini, a compact omni-modal embedding model designed to integrate multiple data types without compromising text retrieval quality. The model, available in 0.9B and 2.3B parameter versions, maps text, speech, audio, images, and video into a shared space by training media encoders to match the embeddings of dense captions generated from the media itself. This approach preserves the original text embedding capabilities while adding new modalities, making the smaller version suitable for on-device applications and competitive with larger, closed-source models. AI

IMPACT Enables more efficient multi-modal AI systems by reducing model size without sacrificing text retrieval performance.

RANK_REASON The cluster describes a new research paper detailing a novel model architecture and training methodology for multi-modal embeddings.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Omni-Embed-Mini integrates multiple modalities without text quality loss

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The cluster describes a new research paper detailing a novel model architecture and training methodology for multi-modal embeddings.
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COVERAGE [2]

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

    Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation

    Extending a text embedding model to new modalities typically degrades text retrieval quality, and existing omni-modal embedders compensate with multi-billion parameters. We present Omni-Embed-Mini, a 0.9B-parameter model that maps text, speech, audio, images, video, and visually-…

  2. arXiv cs.CV TIER_1 English(EN) · Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Sahal Shaji Mullappilly, Ivan Laptev, Hisham Cholakkal ·

    Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation

    arXiv:2610.02148v1 Announce Type: new Abstract: Extending a text embedding model to new modalities typically degrades text retrieval quality, and existing omni-modal embedders compensate with multi-billion parameters. We present Omni-Embed-Mini, a 0.9B-parameter model that maps t…

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