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

arXiv:2610.12256 · cs.CV, cs.AI, cs.IR · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.AI, cs.IR

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/sony/syn-omni

Terminology

• Adaptation — 28× in this paper · explained in 1 episode
• Benchmark — 25× in this paper · explained in 2 episodes
• Synergy Margin Loss — 14× in this paper
• Embeddings — 13× in this paper · explained in 2 episodes
• Progressive Synergy Routing — 13× in this paper
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Alignment — 4× in this paper · explained in 1 episode
• Contrastive Learning — 4× in this paper · explained in 5 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Decoupling — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Mixture of Experts — 2× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Stiefel manifold — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes
• While Binary Cross-Entropy — 1× in this paper

Sources

• MusicLM: Generating Music From Text
• Qwen2.5-VL Technical Report
• A Short Note on the Kinetics-700 Human Action Dataset
• Towards Universal Video Retrieval: Generalizing Video Embedding via Synthesized Multimodal Pyramid Curriculum
• E5-V: Universal Embeddings with Multimodal Large Language Models
• MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• MCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval
• Scaling Audio-Text Retrieval with Multimodal Large Language Models
• Qwen2.5-Omni Technical Report
• Omni-Embed-Nemotron: A Unified Multimodal Retrieval Model for Text, Image, Audio, and Video
• MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed
• Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalities

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