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OmniUE unifies text, video, and audio embeddings with interactive querying

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the Omni-Interactive Universal Embedder (OmniUE), a novel system designed to unify embeddings across text, video, and audio modalities. Unlike previous models that primarily focused on text and images, OmniUE utilizes dedicated learnable tokens and an omni-LLM to process diverse user interactions, including visual regions of interest and audio spans. To assess its capabilities, a new benchmark called OmniCHOIR was developed, which evaluates omni-interactive compositional audio retrieval. OmniUE demonstrated significant performance improvements over existing methods on various benchmarks, including a substantial 24.1% gain on the OmniCHOIR benchmark. AI

IMPACT This research advances multimodal representation learning, potentially enabling more versatile and interactive AI systems for processing diverse data types.

RANK_REASON The cluster contains an academic paper detailing a new multimodal embedding model and benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

OmniUE unifies text, video, and audio embeddings with interactive querying

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The cluster contains an academic paper detailing a new multimodal embedding model and benchmark. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wei-Yao Wang, Kazuya Tateishi, Shuyang Cui, Christian Simon, Takashi Shibuya, Shusuke Takahashi, Yuki Mitsufuji ·

    Omni-Interactive Universal Embedder

    arXiv:2608.27044v1 Announce Type: new Abstract: Multimodal representation learning has been shifting from traditional two-tower architectures to large language model (LLM)-based embedders due to their strong instruction-following capabilities. Despite this progress, existing appr…

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