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/nikhithakarennagari/Git
https://github.com/nikkkkhil/lane-detection-using-lanenet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/ningdez/Tianchi_Cancer_303
https://github.com/nithinksath96/MMdetection_TensorRT_FP16
https://github.com/nithinksath96/mmdetection
https://github.com/nitin35/masktrackrcnn
https://github.com/nku-shengzheliu/PaddlePaddle-Swin-Transformer-V2
https://github.com/noahj08/DeepConnotation
https://github.com/noelcodes/Mask_RCNN
https://github.com/novav/DetectoRS_Colab
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
https://github.com/nv-nguyen/pizza
UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place Solution
https://github.com/nyoki-mtl/pytorch-discriminative-loss
Semantic Instance Segmentation with a Discriminative Loss Function
https://github.com/o-evgeny/MRCNN_DeepFashion2
https://github.com/oarriaga/paz
https://github.com/octree-nn/ocnn-pytorch
https://github.com/omkar13/MaskTrack
https://github.com/oneformer3d/oneformer3d
OneFormer3D: One Transformer for Unified Point Cloud Segmentation
https://github.com/opconty/pixellink_keras
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmaction2
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmpretrain
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
https://github.com/open-mmlab/mmrotate
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/open-mmlab/mmsegmentation
https://github.com/open-mmlab/mmsegmentation/tree/main/configs/vpd
Unleashing Text-to-Image Diffusion Models for Visual Perception
https://github.com/open-mmlab/mmselfsup
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
https://github.com/open-mmlab/mmyolo
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/openrobotlab/ov_parts
https://github.com/osmr/imgclsmob
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/owena11/papers
https://github.com/oxcsaml2019/multitask-learning
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/pandamax/Lane-Detection-Based-PINet
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/panjianning/cbnet
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.