arXiv (All)
open access
EvoIR-Agent: Self-Evolving Image Restoration Agentic System via Experience-Driven Learning
Multimodal Large Language Model (MLLM)-driven image restoration agent demonstrates effectiveness in degradation coupling scenarios by flexibly selecting tools and determining removal orders. However, its zero-shot planning often fails without experience, necessitating severe trial-and-error overhead
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
- ImIR: Image-Instruction Tuning for All-in-One Image Restoration#1051760
- Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging#373391
- HyperErase: Scale-Calibrated Hypernetwork for Multi-Concept Erasure in Text-to-Image Models#1105712
- Fast and Accurate Image Restoration and Generation with Rank Enhanced Linear Attention#792631
- Fast and Accurate Image Restoration and Generation with Rank Enhanced Linear Attention#788631
Record · ID 1143775
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