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Image Editing
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Neural-Driven Image Editing
UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation
ImgEdit: A Unified Image Editing Dataset and Benchmark
Emerging Properties in Unified Multimodal Pretraining
StableMotion: Repurposing Diffusion-Based Image Priors for Motion Estimation
Image Restoration via Multi-domain Learning
Step1X-Edit: A Practical Framework for General Image Editing
Fashion-RAG: Multimodal Fashion Image Editing via Retrieval-Augmented Generation
Retinex-guided Histogram Transformer for Mask-free Shadow Removal
FASR-Net: Unsupervised Shadow Removal Leveraging Inherent Frequency Priors
Leveraging Contrast Information for Efficient Document Shadow Removal
AnyEdit: Edit Any Knowledge Encoded in Language Models
Prompt-Aware Controllable Shadow Removal
Towards Hard and Soft Shadow Removal via Dual-Branch Separation Network and Vision Transformer
Detail-Preserving Latent Diffusion for Stable Shadow Removal
Controlling the Latent Diffusion Model for Generative Image Shadow Removal via Residual Generation
Regional Attention for Shadow Removal
WavShadow: Wavelet Based Shadow Segmentation and Removal
ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal
Generative Portrait Shadow Removal
OmniSR: Shadow Removal under Direct and Indirect Lighting
GroupDiff: Diffusion-based Group Portrait Editing
SoftShadow: Leveraging Penumbra-Aware Soft Masks for Shadow Removal
Shadow Removal Refinement via Material-Consistent Shadow Edges
DPDEdit: Detail-Preserved Diffusion Models for Multimodal Fashion Image Editing
SelfDRSC++: Self-Supervised Learning for Dual Reversed Rolling Shutter Correction
Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal
Diff-Shadow: Global-guided Diffusion Model for Shadow Removal
Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey
UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal
Cross-Modal Spherical Aggregation for Weakly Supervised Remote Sensing Shadow Removal
In-Context Editing: Learning Knowledge from Self-Induced Distributions
ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal
Paint by Inpaint: Learning to Add Image Objects by Removing Them First
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.