arXiv Computer Vision
Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision
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Fast and Accurate Image Restoration and Generation with Rank Enhanced Linear Attention
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Semantic Generative Tuning for Unified Multimodal Models
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UniSpace: Unified Visual Representation and Scalable Multimodal Modeling
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From Dense Prediction to Visual Editing: Structured Supervision for Unified Image and Video Creation
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From Tokens to Trajectories: Parameter-Efficient Continuous Adaptation of Unified Multimodal Models
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VoT: Vision-of-Thought for Unified Multimodal Representation Alignment
The paper introduces Vision-of-Thought (VoT), a framework that inserts a discrete visual-thinking layer between vision‑language models (VLMs) and diffusion transformers (DiTs). Instead of using VLMs solely as text encoders, they act as multimodal planners that generate VoT tokens—high‑level visual plans such as objects and layouts—before pixel rendering. A specialized VoT tokenizer is trained with a closed‑loop objective combining VLM alignment, feature reconstruction, and vector‑quantization losses, ensuring the tokens are semantically readable by the VLM while preserving necessary visual information. Experimental results show that VoT improves semantic alignment and offers a structured, interpretable interface for controllable generation.