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Junmo Kim

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#artificial intelligence Preprint Oct 2026

Syn-Omni: Structured Specialization and Progressive Collaboration for Omnimodal Embeddings

Omnimodal embeddings naturally involve both shared representations and modality-specific features across heterogeneous inputs. However, existing omnimodal embedding methods often rely on a single shared parameter space over mixed-modality data, limiting structural separation between universal and modality-specific repr...

Youngtaek Oh, Qi-Yu Wu, Hiromi Wakaki et al. · 0 citations
Preprint Aug 2026

MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations

Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capabil...

Jongsuk Kim, Qi-Yu Wu, Zhuoyuan Mao et al. · 0 citations

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