arXiv (All)
open access
Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation
Text-to-image models have made remarkable progress in visual fidelity and instruction following, yet complex compositional generation remains challenging. Existing agentic approaches address this limitation through iterative generation and editing, adapting their actions based on interaction history
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
- APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation#1343288
- What Drives Compositional Generalization in Visual Generative Models? The Importance of Continuous Training Objectives#1156214
- Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models#924379
- Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models#920148
- TPSO: Training-Free Diverse Image Generation via Semantic Prompt Embedding Optimization#799913
Record · ID 1327814
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