arXiv:2610.37364 · v1
EmbeddingGemma 2: Unified and Efficient Multimodal Representations
摘要
We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space. Our approach introduces improvements such as simplified per-layer embeddings and a multi-stage curriculum with modality and objective progression which ensure strong quality across unimodal, cross-modal, and mixed-modal evaluations. Notably, in comprehensive embedding benchmarks across all modalities, EmbeddingGemma 2 establishes a new Pareto frontier, outperforming both specialist models and models two to twelve times larger. Furthermore, our design offers flexibility for resource-constrained environments, supporting selective modality encoder loading, adjustable vision token budgets, truncatable embedding dimensions, and quantized weights. We present ablations analyzing key decisions and release EmbeddingGemma 2 to promote further research.
作者备注：28 pages. Models are available on HuggingFace (at https://huggingface.co/google/embeddinggemma-2) and LiteRT (at https://huggingface.co/litert-community/embeddinggemma-2-740m-litert-lm)