Papers with Code 아카이브
Instance Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 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/SAtacker/PosenetTflite
https://github.com/STomoya/ResNeSt
https://github.com/SUYEgit/Surgery-Robot-Detection-Segmentation
https://github.com/SatyamGaba/lt_od
https://github.com/SeHwanJoo/mmdetection_vinbig
https://github.com/SebasGarcia08/sku110-dense-object-mmdetection
https://github.com/SeniorDev009/ONNX-project
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/Sense-X/TSD
1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation
https://github.com/SeonghwanSeo/RxnFlow
PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
https://github.com/SeungyounShin/LaneNet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/SfTI-Robotics/ROS-label-node
https://github.com/SforAiDl/vformer
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
https://github.com/ShangHua-Gao/G2L-search
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
https://github.com/SharadGitHub/Self-Supervised-Autoencoder
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/SharifElfouly/easy-model-zoo
https://github.com/Shrajan/AAAI-2022
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/Shrajan/IB_U_Nets
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/ShreyasArthur/StyleGAN-2-with-Urban-Plans
https://github.com/Shun14/panopticFPN-paddle
https://github.com/SiavashCS/sgan_simple
https://github.com/SilvioGiancola/maskrcnn-benchmark
Is Heuristic Sampling Necessary in Training Deep Object Detectors?
https://github.com/Sim-ai/mmdetection2
https://github.com/SimonKohl/probabilistic_unet
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
https://github.com/SiriusKY/SceneTextDetector
https://github.com/SonginCV/GMPHD_MAF
https://github.com/SonginCV/GMPHD_SAF
https://github.com/SonwYang/building-extraction
Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells
https://github.com/SpaceView/Yolact_EfficientNet
https://github.com/Stanch-LX/mmdetection
https://github.com/StephenEkaputra/Mask_RCNN-TinyPascalVOC
https://github.com/SwinTransformer/Transformer-SSL
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/SysCV/vmt
Video Mask Transfiner for High-Quality Video Instance Segmentation
https://github.com/TakShimoda/CP8322-Deep-Learning-in-Computer-Vision-Course-Project
https://github.com/TangDL/mmdetection
https://github.com/TeamA2020/Practice
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.