Papers with Code 아카이브
Segmentation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yk-szk/SPIE2020
공식 코드https://github.com/yl596/otas
공식 코드OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation
https://github.com/ymli39/ACEnet-for-Neuroanatomy-Segmentation
공식 코드ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
https://github.com/ymp5078/semantic-stacking
공식 코드S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
https://github.com/ymy-k/hi-sam
공식 코드Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation
https://github.com/yoctta/xpaste
공식 코드X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion
https://github.com/yongchengyao/cartimorph
공식 코드CartiMorph: a framework for automated knee articular cartilage morphometrics
https://github.com/yonghaoxu/se-gan
공식 코드https://github.com/yonghaoxu/ut-kd
공식 코드Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data
https://github.com/yonghengsun1997/odada
공식 코드https://github.com/yongliu20/UniLSeg
공식 코드Universal Segmentation at Arbitrary Granularity with Language Instruction
https://github.com/youhuang67/high-resolution-segment-anything
공식 코드HRSAM: Efficient Interactive Segmentation in High-Resolution Images
https://github.com/youhuang67/interformer
공식 코드https://github.com/younggon2/Research-Segmentation-Lung-CXR-COVID19
공식 코드Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis
https://github.com/youngsean/unseenobjectswithmeanshift
공식 코드Mean Shift Mask Transformer for Unseen Object Instance Segmentation
https://github.com/youngwanLEE/CenterMask
공식 코드https://github.com/youngwanLEE/MPViT
공식 코드https://github.com/youquanl/m3net
공식 코드https://github.com/youshanzhang/lung_segmentation
공식 코드https://github.com/yoxu515/aot-benchmark
공식 코드Scalable Video Object Segmentation with Identification Mechanism
https://github.com/yoxu515/mits
공식 코드Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation
https://github.com/ysmei97/multimodal_pci_mask
공식 코드Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation
https://github.com/yuan-12138/mi-segnet
공식 코드MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization
https://github.com/yuanshuai0914/fusu
공식 코드https://github.com/yuanzhang7/pathfl
공식 코드PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation
https://github.com/yuexin18/morseg-cam-sam
공식 코드Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation
https://github.com/yuggiehk/care-ego
공식 코드CaRe-Ego: Contact-aware Relationship Modeling for Egocentric Interactive Hand-object Segmentation
https://github.com/yuggiehk/ormnet
공식 코드CaRe-Ego: Contact-aware Relationship Modeling for Egocentric Interactive Hand-object Segmentation
https://github.com/yuhangzang/FASA
공식 코드FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation
https://github.com/yuhoo0302/segment-anything-model-for-medical-images
공식 코드https://github.com/yuhuan-wu/JCS
공식 코드JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
https://github.com/yuhuan-wu/lrformer
공식 코드https://github.com/yuk6heo/GIS-RAmap
공식 코드Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
https://github.com/yuk6heo/IVOS-ATNet
공식 코드Interactive Video Object Segmentation Using Global and Local Transfer Modules
https://github.com/yuki-11/csbsr
공식 코드Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.