Papers with Code 아카이브
Instance Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 36
- 보존된 평가 기록
- 338
Instance Segmentation is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.
Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/13952522076/mmdet1
https://github.com/2020-sk49/VFOST
https://github.com/2023-MindSpore-1/ms-code-105
Relieving Long-tailed Instance Segmentation via Pairwise Class Balance
https://github.com/2023-MindSpore-1/ms-code-118
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-143
Sparse Instance Activation for Real-Time Instance Segmentation
https://github.com/2023-MindSpore-1/ms-code-144
Temporally Efficient Vision Transformer for Video Instance Segmentation
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/Contents
https://github.com/2023-MindSpore-1/ms-code-162
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-207
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FCANet
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HMR
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/IRN
IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/ResNeSt50
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/Yolact%2B%2B
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tinydarknet
https://github.com/2023-MindSpore-1/ms-code-222/tree/main/pointrend
https://github.com/2023-MindSpore-1/ms-code-34
https://github.com/2023-MindSpore-1/ms-code-53
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/Pix2PixHD1
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/Unet3d
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/WGAN_GP
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/Yolact%2B%2B
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tinydarknet
https://github.com/2023-MindSpore-4/Code-5/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-4/Code1/tree/main/Efficientnet
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/2023-MindSpore-4/Code10/tree/main/Efficientnet
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/2023-MindSpore-4/Code10/tree/main/MaskRCNN
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/swin
https://github.com/2023-MindSpore-4/Code14/tree/main/ResNeSt50
https://github.com/2023-MindSpore-4/Code14/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/res2net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.