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Image Editing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 2
- 보존된 평가 기록
- 12
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jinyeying/DeS3_Deshadow
공식 코드DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity
https://github.com/jinyeying/S-Aware-network
공식 코드https://github.com/josauder/glodismo
공식 코드Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery
https://github.com/jx-zhong-for-academic-purpose/kinet
공식 코드https://github.com/kilito777/ShaDocFormer
공식 코드https://github.com/lgm70/smug
공식 코드SMUG: Towards robust MRI reconstruction by smoothed unrolling
https://github.com/lizhh268/shadowmaskformer
공식 코드ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal
https://github.com/ljzycmd/bsrsc
공식 코드Learning Adaptive Warping for Real-World Rolling Shutter Correction
https://github.com/ljzycmd/dfrsc
공식 코드Rolling Shutter Correction with Intermediate Distortion Flow Estimation
https://github.com/lmhieu612/SID
공식 코드https://github.com/lsky96/gcnwmmse
공식 코드Coordinated Sum-Rate Maximization in Multicell MU-MIMO with Deep Unrolling
https://github.com/mateuszgabor/nn_networks
공식 코드https://github.com/maxwass/pygsl
공식 코드https://github.com/maybe198376/LEARN-Plus-Plus
공식 코드LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
https://github.com/mneyrane/as-nesta-net
공식 코드NESTANets: Stable, accurate and efficient neural networks for analysis-sparse inverse problems
https://github.com/montecarluo/diff-shadow
공식 코드Diff-Shadow: Global-guided Diffusion Model for Shadow Removal
https://github.com/movingforward100/shadow_r
공식 코드ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer
https://github.com/n-kubiak/s3r-net
공식 코드S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal
https://github.com/naoto0804/SynShadow
공식 코드Learning from Synthetic Shadows for Shadow Detection and Removal
https://github.com/ngrxmu/lab-net
공식 코드LAB-Net: LAB Color-Space Oriented Lightweight Network for Shadow Removal
https://github.com/niika/SIDAR
공식 코드https://github.com/nkicsl/resfusion
공식 코드https://github.com/noamjanco/unrolling_synchronization
공식 코드https://github.com/odlgroup/odl
공식 코드https://github.com/osu-nlp-group/magicbrush
공식 코드MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing
https://github.com/pku-yuangroup/imgedit
공식 코드https://github.com/pkunlp-icler/ultraedit
공식 코드UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
https://github.com/pomonam/Self-Tuning-Networks
공식 코드Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
https://github.com/sanghviyashiitb/poisson-deblurring
공식 코드Photon Limited Non-Blind Deblurring Using Algorithm Unrolling
https://github.com/sckangz/RGC
공식 코드https://github.com/seungjun45/Water-Filling
공식 코드Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal
https://github.com/shagunsodhani/consistent-dynamics
공식 코드Learning Powerful Policies by Using Consistent Dynamics Model
https://github.com/shangwei5/selfdrsc_plusplus
공식 코드SelfDRSC++: Self-Supervised Learning for Dual Reversed Rolling Shutter Correction
https://github.com/siml3/ru-net
공식 코드RU-Net: Regularized Unrolling Network for Scene Graph Generation
https://github.com/siwangzhou/deepbcs
공식 코드Multi-Channel Deep Networks for Block-Based Image Compressive Sensing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.