Hugging Face Trending Papers
ImIR: Image-Instruction Tuning for All-in-One Image Restoration
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arXiv Computer Vision
Sep 23
ImIR: Image-Instruction Tuning for All-in-One Image Restoration
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.
arXiv AI
Aug 11
Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance
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.
arXiv AI
Aug 11
UniSpace: Unified Visual Representation and Scalable Multimodal Modeling
arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.
arXiv Computer Vision
6d ago
TaskIR: Task-Driven Image Restoration via Degradation Adaptation and Task Feedback
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 Computer Vision
3d ago
EvoIR-Agent: Self-Evolving Image Restoration Agentic System via Experience-Driven Learning
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 AI
Jul 22
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
arXiv:2607. 19064v1 Announce Type: cross Abstract: Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy.