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
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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/Arthur-Shi/py-faster-rcnn
https://github.com/AshishSingh2261/Pedestrian_Instance_Segmentation
https://github.com/Asthestarsfalll/ResNeSt-MegEngine
https://github.com/Asthestarsfalll/VAN-MegEngine
https://github.com/Autonise-AI/Text-Recognition
https://github.com/Autonomous-Visual-Perception/detection-benchmark
https://github.com/AvivNavon/AuxiLearn
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/BIGBALLON/distribuuuu
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/CoaT
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/Focal_Transformer
Focal Self-attention for Local-Global Interactions in Vision Transformers
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/HaloNet
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/XCiT
https://github.com/BYLi-AI/poly-yolo
Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3
https://github.com/Bakikii/stylegan2-pytorch23
https://github.com/Banconxuan/RTM3D
Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training
https://github.com/Biantian/MscProject
https://github.com/BigThreeMI/Utils
https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning
https://github.com/BlackAngel1111/Fast-RCNN
https://github.com/Bo396543018/picodet_repro
https://github.com/Boese0601/DUT_AI_2020_mmdetection
https://github.com/BorealisAI/PROVIDE
Multi-Object Representation Learning with Iterative Variational Inference
https://github.com/BorealisAI/Spatio-Temporal
Multi-Object Representation Learning with Iterative Variational Inference
https://github.com/BrilliantYuKaimin/XCiT-PaddlePaddle
https://github.com/BugBoy13/sd-maskrcnn
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data
https://github.com/BupyeongHealer/Mask_RCNN_tf_2.x
https://github.com/Burf/ResNeSt-Tensorflow2
https://github.com/Burf/SwinTransformer-Tensorflow2
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Burf/tfdetection
https://github.com/CASIA-IVA-Lab/ISP-reID
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/CHAITron/sketchdeco-code
DreamColour: Controllable Video Colour Editing without Training
https://github.com/CQU-ADHRI-Lab/MI-DETR
DI-MaskDINO: A Joint Object Detection and Instance Segmentation Model
https://github.com/CS-GangXu/TMNet
https://github.com/CSAILVision/semantic-segmentation-pytorch
Deep High-Resolution Representation Learning for Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.