Papers with Code 아카이브
Image Editing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/stepfun-ai/step1x-edit
공식 코드Step1X-Edit: A Practical Framework for General Image Editing
https://github.com/stonezwr/ST-RSBP
공식 코드Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
https://github.com/storymy/take-off-eyeglasses
공식 코드Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data
https://github.com/sunke123/FW-Net
공식 코드Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding
https://github.com/ternencewu123/parcel
공식 코드PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging
https://github.com/timothybrooks/instruct-pix2pix
공식 코드InstructPix2Pix: Learning to Follow Image Editing Instructions
https://github.com/trustai/deepnnc
공식 코드https://github.com/tsingqguo/exposure-fusion-shadow-removal
공식 코드https://github.com/tsingqguo/inpaint4shadow
공식 코드https://github.com/vinthony/ghost-free-shadow-removal
공식 코드Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
https://github.com/vis-opt-group/ruas
공식 코드Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision
https://github.com/vita-group/hyperlista
공식 코드https://github.com/wushanshan/L1AE
공식 코드Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
https://github.com/xinrui014/softshadow
공식 코드SoftShadow: Leveraging Penumbra-Aware Soft Masks for Shadow Removal
https://github.com/xuyimin0926/sadc
공식 코드https://github.com/yairkit/flowstep3d
공식 코드FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation
https://github.com/yhao-z/t2lr-net
공식 코드https://github.com/yingqinghe/shadow-removal-via-generative-priors
공식 코드https://github.com/yunfanLu/EG-RSDBI
공식 코드UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation
https://github.com/yunfanlu/uniinr
공식 코드UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation
https://github.com/yunshihuang/unfoldedvba
공식 코드Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
https://github.com/zhangbaijin/sam-helps-shadow
공식 코드SAM-helps-Shadow:When Segment Anything Model meet shadow removal
https://github.com/zhangbaijin/spa-former-shadow-removal
공식 코드SpA-Former: Transformer image shadow detection and removal via spatial attention
https://github.com/zhangbaijin/spatial-transformer-shadow-removal
공식 코드SpA-Former: Transformer image shadow detection and removal via spatial attention
https://github.com/zhangxydlut/Neural-Image-Re-Exposure
공식 코드https://github.com/zhling2020/RIS-GAN
공식 코드RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
https://github.com/zipei-chen/canet
공식 코드https://github.com/zysxmu/hqss
공식 코드https://github.com/zzh-tech/RSCD
공식 코드Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes
https://github.com/ByteDance-Seed/Bagel
https://github.com/CNugteren/CLBlast
https://github.com/CV-Reimplementation/AEFNet-Reimplementation
https://github.com/CV-Reimplementation/BMNet-Reimplementation
https://github.com/CV-Reimplementation/DHAN-reimplementation
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
https://github.com/CV-Reimplementation/TraditionalDocumentShadowRemoval
Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.