https://github.com/cs-chan/Total-Text-Dataset
공식 코드Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
Papers with Code 아카이브
2D Semantic Segmentation
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/cscyqj/miccai23-protocontra-sfda
공식 코드https://github.com/csguoh/lemma
공식 코드Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement
https://github.com/csimo005/summit
공식 코드SUMMIT: Source-Free Adaptation of Uni-Modal Models to Multi-Modal Targets
https://github.com/csiro-robotics/WildScenes
공식 코드WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments
https://github.com/csmliu/adanec
공식 코드https://github.com/csyfjiang/mlla-unet
공식 코드MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation
https://github.com/cszyzhang/riconv2
공식 코드RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
https://github.com/cugbrs/MLRSNet
공식 코드https://github.com/cuhk-aim-group/diffrect
공식 코드DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation
https://github.com/curli-quan/slide-sam
공식 코드https://github.com/cuthbert-huang/cc-net
공식 코드Complementary consistency semi-supervised learning for 3D left atrial image segmentation
https://github.com/cuthbert-huang/cpcanet
공식 코드Channel prior convolutional attention for medical image segmentation
https://github.com/cv-rits/densemtl
공식 코드DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning
https://github.com/cvg/labelmaker
공식 코드ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
https://github.com/cvlab-kaist/SOLA
공식 코드Referring Video Object Segmentation via Language-aligned Track Selection
https://github.com/cvlab-stonybrook/tokensparse-for-medseg
공식 코드https://github.com/cvmi-lab/pla
공식 코드https://github.com/cvmi-lab/slotmim
공식 코드A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
https://github.com/cwlee00/mfp
공식 코드MFP: Making Full Use of Probability Maps for Interactive Image Segmentation
https://github.com/cxfyxl/viptr
공식 코드SVIPTR: Fast and Efficient Scene Text Recognition with Vision Permutable Extractor
https://github.com/cxh-research/smaformer
공식 코드SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation
https://github.com/cxqnet/masseg
공식 코드https://github.com/cxx226/dpf
공식 코드https://github.com/cy-xu/cosmic-conn
공식 코드Interactive Segmentation and Visualization for Tiny Objects in Multi-megapixel Images
https://github.com/cyb08/mambaclinix-pytorch
공식 코드https://github.com/cybonic/misagriculture
공식 코드Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches
https://github.com/cybus123456/hich-it-datasets
공식 코드https://github.com/cyh4/FCTSFN
공식 코드A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation
https://github.com/cys1102/fba-net
공식 코드https://github.com/cyt1202/byocl
공식 코드BYOCL: Build Your Own Consistent Latent with Hierarchical Representative Latent Clustering
https://github.com/cyuan-sjtu/vit-asym
공식 코드Surgical Scene Segmentation by Transformer With Asymmetric Feature Enhancement
https://github.com/d-rothen/bmhrl
공식 코드https://github.com/dafne-imaging/dafne
공식 코드https://github.com/dair-iitd/c2fi4cv
공식 코드https://github.com/dair-iitd/guided-prompting-sam
공식 코드Guided Prompting in SAM for Weakly Supervised Cell Segmentation in Histopathological Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.