Papers with Code 아카이브
Instance Segmentation
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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/mahdi-darvish/centermask
https://github.com/manideep03/Brain-Tumor-Segmentation
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/manthanpatel2/Object-Detection-on-a-Frame
https://github.com/manuelhz/dissertation
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/map-learning/tl2la
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/markson14/WheatDet
Is Heuristic Sampling Necessary in Training Deep Object Detectors?
https://github.com/marrlab/dinobloom
NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images
https://github.com/martinsbruveris/tensorflow-image-models
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
https://github.com/masamitsu-murase/deform_conv2d_onnx_exporter
https://github.com/mathilde-b/SFDA
Common Limitations of Image Processing Metrics: A Picture Story
https://github.com/mattdeitke/cvpr-buzz
VirTex: Learning Visual Representations from Textual Annotations
https://github.com/maudzung/TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/maudzung/TTNet-Realtime-for-Table-Tennis-Pytorch
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/maxfrei750/DeepParticleNet
https://github.com/maxfrei750/FibeR-CNN
https://github.com/mayank-git-hub/Text-Recognition
https://github.com/mcg-nju/adamixer
https://github.com/megvii-research/basecls/tree/main/zoo/public/hrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/megvii-research/basecls/tree/main/zoo/public/swin
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/mengfu188/mmdetection.bak
https://github.com/mhw32/contrastive-ring-public
Conditional Negative Sampling for Contrastive Learning of Visual Representations
https://github.com/miaohua1982/simple_fasterrcnn_pytorch
https://github.com/mic-dkfz/medicaldetectiontoolkit
https://github.com/michaelisc/cluttered-omniglot
https://github.com/microsoft/TokenMixers
https://github.com/microsoft/VisionLongformerForObjectDetection
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
https://github.com/microsoft/esvit
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
https://github.com/milliondegree/semantic-segmentation-tensorflow
https://github.com/min2209/dwt
https://github.com/mindspore-ai/models/tree/master/official/cv/maskrcnn
https://github.com/mindspore-ai/models/tree/master/official/cv/yolov5
https://github.com/mindspore-ai/models/tree/master/research/cv/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/ResNeSt50
https://github.com/mindspore-ai/models/tree/master/research/cv/Yolact++
https://github.com/mindspore-ai/models/tree/master/research/cv/faster_rcnn_dcn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.