Papers with Code 아카이브
2D Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 11
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ge-xing/hybridmim
공식 코드HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation
https://github.com/ge-xing/segmamba
공식 코드SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
https://github.com/gengshan-y/monodepth-uncertainty
공식 코드https://github.com/genintel/datasetnerf
공식 코드DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fields
https://github.com/geoaigroup/geoai-ecrs2023
공식 코드https://github.com/geohai/sea-ice-segment
공식 코드Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions
https://github.com/georg3tom/RoadTextVQA
공식 코드https://github.com/georgebatch/kvasir-seg
공식 코드https://github.com/geshang777/FOCUS
공식 코드https://github.com/geshang777/Seg-R1
공식 코드Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
https://github.com/gewelsji/errnet
공식 코드Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
https://github.com/gewu-lab/crab
공식 코드Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation
https://github.com/gewu-lab/mwafm
공식 코드https://github.com/giakoumoglou/synco
공식 코드https://github.com/giaylenia/OCTA_segm_study
공식 코드https://github.com/giddyyupp/AdvSegLoss
공식 코드https://github.com/gift-surg/HighRes3DNet
공식 코드https://github.com/gigantenbein/UNet-Zoo
공식 코드https://github.com/gitycc/traditional-chinese-text-recogn-dataset
공식 코드Traditional Chinese Synthetic Datasets Verified with Labeled Data for Scene Text Recognition
https://github.com/givkashi/focal-unet
공식 코드Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation
https://github.com/gkw0010/eisost-sim2real-dataset-release
공식 코드Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs
https://github.com/gkw0010/endochat
공식 코드EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery
https://github.com/gmum/proto-segmentation
공식 코드ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
https://github.com/gndlwch2w/msvm-unet
공식 코드MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation
https://github.com/goblin327/segkan
공식 코드SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies
https://github.com/google-research-datasets/2.5vrd
공식 코드https://github.com/google-research/big_vision
공식 코드How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/google-research/cad-estate
공식 코드https://github.com/google-research/scenic
공식 코드Unified Visual Relationship Detection with Vision and Language Models
https://github.com/google-research/snap
공식 코드SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
https://github.com/google-research/vision_transformer
공식 코드How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/google/quickshift
공식 코드Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
https://github.com/gorilla-lab-scut/sstnet
공식 코드Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks
https://github.com/gpt4vision/OvSGTR
공식 코드https://github.com/gpt4vision/gpt4sgg-llm
공식 코드GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.