arXiv (All)
open access
VoT: Vision-of-Thought for Unified Multimodal Representation Alignment
Current text-to-image systems typically employ a "text encoder plus diffusion decoder" paradigm, in which text semantics directly modulate continuous latent noise. Despite their success, these methods lack an explicit, interpretable intermediate representation that effectively bridges high-level lin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising#819321
- V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising#809328
- PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion#1155418
- SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport#1050824
- Scaffold Then Internalize: Representation Injection for Diffusion Transformers#1129885
Record · ID 984709
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive