Virtual Encoders in Multimodal Transformers

arXiv:2609.26513 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-24

Terminology

• Transformer — 52× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Alignment — 8× in this paper · explained in 1 episode
• Linear Probing — 4× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 2× in this paper
• Modality Gap — 2× in this paper · explained in 4 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Activation Patching — 1× in this paper · explained in 2 episodes
• Audio Spectrum Transformer — 1× in this paper
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Intrinsic Dimension — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Qwen3-VL Technical Report
• Chameleon: Mixed-Modal Early-Fusion Foundation Models
• Qwen2-Audio Technical Report
• Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• Emu3: Next-Token Prediction is All You Need
• Qwen3-Omni Technical Report

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