SkillIR is a skill-guided framework for agentic image restoration that represents restoration experience as degradation-centered action evidence rather than full tool-use trajectories. It consolidates context-dependent action outcomes into scene-aware restoration skills, guiding one bounded action at a time within a verified residual-state loop. Experiments on synthetic and real-world multi-degradation datasets show that SkillIR improves restoration quality and enables more reliable and effective tool use.
arXiv:2605.22208v3 Announce Type: replace Abstract: Multimodal Large Language Model (MLLM)-driven image restoration agent demonstrates effectiveness in degradation coupling scenarios by flexibly sele...
arXiv:2609.31170v1 Announce Type: new Abstract: Task-driven image restoration aims to improve both image quality and downstream task performance. However, existing methods predominantly focus on sing...
arXiv:2608.30305v1 Announce Type: new Abstract: Image restoration models are typically trained with a fixed set of capabilities. When new restoration requirements emerge, existing solutions usually t...
arXiv:2608. 09482v1 Announce Type: cross Abstract: All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model.
Remote sensing images acquired by unmanned aerial vehicles (UAVs) and satellites are often degraded by adverse weather, illumination variation, and imaging artifacts, which may co-occur and jointly induce global distribution shifts and local structural corruption. Although All-in-One image restoration offers an appealing unified alternative to task-specific pipelines, existing methods still suffer from weak or implicit degradation cues and parameter redundancy caused by full-rank multi-expert designs with overlapping restoration behaviors.
arXiv:2609.13791v1 Announce Type: new Abstract: Recognition pipelines typically adopt a restore-then-recognize workflow, yet decades of experience show that generating visually pleasing images seldom...
arXiv:2608. 06699v1 Announce Type: new Abstract: Agentic multimodal large language models (MLLMs) extend multimodal perception and reasoning with planning, tool use, and interaction in dynamic environments.
arXiv:2607. 01709v1 Announce Type: new Abstract: Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently.
arXiv:2608.29037v1 Announce Type: cross Abstract: Real-world degradations such as blur, shadow, distortion, and moire patterns severely impair the document question-answering capabilities of Multimod...
arXiv:2607. 13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps.
arXiv:2609.40048v1 Announce Type: new Abstract: Ultra-long video temporal grounding requires balancing long-range evidence search with fine-grained event understanding under a limited visual budget,...
The paper introduces ImIR, a method that tunes a large pretrained image‑editing model for all‑in‑one image restoration by replacing text prompts with continuous image‑derived instructions. The approach uses a lightweight token mapper to shift the degraded image’s vision‑language embedding toward that of a clean image, enabling a single adapter to handle six restoration tasks in about three hours on one GPU. ImIR outperforms text conditioning in matched comparisons and supports task‑agnostic restoration without requiring a degradation label.