e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings
Modern information systems often involve different types of items, e.g., a text query, an image, a video clip, or an audio segment. This motivates omni-modal embedding models that map heterogeneous modalities into a shared space for direct comparison. However, most recent omni-modal embeddings still rely heavily on implicit alignment inherited from pretrained vision-language model (VLM) backbones. In practice, this causes three common issues: (i) similarity logits have modality-dependent sharpness, so scores are not on a consistent scale; (ii) in-batch negatives become less effective over time because mixed-modality batches create an imbalanced hardness distribution; as a result, many negatives quickly become trivial and contribute little gradient; and (iii) embeddings across modalities show mismatched first- and second-order statistics, which makes rankings less stable. To tackle these problems, we propose e5-omni, a lightweight explicit alignment recipe that adapts off-the-shelf VLMs into robust omni-modal embedding models. e5-omni combines three simple components: (1) modality-aware temperature calibration to align similarity scales, (2) a controllable negative curriculum with debiasing to focus on confusing negatives while reducing the impact of false negatives, and (3) batch whitening with covariance regularization to better match cross-modal geometry in the shared embedding space. Experiments on MMEB-V2 and AudioCaps show consistent gains over strong bi-modal and omni-modal baselines, and the same recipe also transfers well to other VLM backbones. We release our model checkpoint at https://huggingface.co/Haon-Chen/e5-omni-7B.
695fa6f62450f142afb3c58f
Haonan Chen
claimed_verified
2026-01-09T08:35:49.824Z
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/avatars/b72b18130664c1de197c1f8df371aa70.svg
Haonan Chen
Haon-Chen
user
Haon-Chen
695fa6f62450f142afb3c590
Sicheng Gao
695fa6f62450f142afb3c591
Radu Timofte
695fa6f62450f142afb3c592
Tetsuya Sakai
695fa6f62450f142afb3c593
Zhicheng Dou
claimed_verified
2026-02-10T09:27:36.937Z
66f0bf59e9d50ec57febf751
/avatars/be97941e60064e5dd806c6fe9db3c537.svg
Zhicheng Dou
douzc
user
douzc
e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings
Modern information systems often involve different types of items, e.g., a text query, an image, a video clip, or an audio segment. This motivates omni-modal embedding models that map heterogeneous modalities into a shared space for direct comparison. However, most recent omni-modal embeddings still rely heavily on implicit alignment inherited from pretrained vision-language model (VLM) backbones. In practice, this causes three common issues: (i) similarity logits have modality-dependent sharpness, so scores are not on a consistent scale; (ii) in-batch negatives become less effective over time because mixed-modality batches create an imbalanced hardness distribution; as a result, many negatives quickly become trivial and contribute little gradient; and (iii) embeddings across modalities show mismatched first- and second-order statistics, which makes rankings less stable. To tackle these problems, we propose e5-omni, a lightweight explicit alignment recipe that adapts off-the-shelf VLMs into robust omni-modal embedding models. e5-omni combines three simple components: (1) modality-aware temperature calibration to align similarity scales, (2) a controllable negative curriculum with debiasing to focus on confusing negatives while reducing the impact of false negatives, and (3) batch whitening with covariance regularization to better match cross-modal geometry in the shared embedding space. Experiments on MMEB-V2 and AudioCaps show consistent gains over strong bi-modal and omni-modal baselines, and the same recipe also transfers well to other VLM backbones. We release our model checkpoint at https://huggingface.co/Haon-Chen/e5-omni-7B.
Omni-modal embedding models face challenges with modality-dependent similarity scaling, ineffective in-batch negatives, and mismatched statistics across modalities, which are addressed through explicit alignment techniques including temperature calibration, controlled negative curriculum, and batch whitening with covariance regularization.
omni-modal embedding models vision-language models similarity logits in-batch negatives modality-aware temperature calibration negative curriculum debiasing batch whitening covariance regularization cross-modal geometry