arXiv: cs.AI preprintsInternational5 November 2026
When Does Sharing Work? Scaling a Shared SigLIP2 Across Image, Video, Audio, and Audio-Visual Representations
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arXiv:2611.05406v1 Announce Type: cross Abstract: Contrastive pretraining has advanced image-text understanding, yet image, video, and audio encoders are still trained independently, with cross-modal alignment realized mainly through post-hoc binding to a frozen anchor space. We present Omni-Encoder, a general multimodal encoder that processes image, video, audio, and audio-visual inputs within a single SigLIP-2 Vision Transformer with fully shared parameters. Unlike anchor-centric binding or per-modality adapters, all modalities are optimized jointly end-to-end, and the framework is organized
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