Papers with Code 아카이브

Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet2

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

https://github.com/2024-MindSpore-1/Code4/tree/main/SwinTransformer

Self-Supervised Learning with Swin Transformers

https://github.com/2024-MindSpore-1/Code5/tree/main/adelaide_ea

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/2024-MindSpore-1/Code6/tree/main/Auto-DeepLab

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation

https://github.com/2024-MindSpore-1/Code6/tree/main/augvit

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/2024-MindSpore-1/Code6/tree/main/beit

BEiT: BERT Pre-Training of Image Transformers

https://github.com/2024-MindSpore-1/Code7/tree/main/CMT

CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101

Deep Residual Learning for Image Recognition

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1

Deep Residual Learning for Image Recognition

https://github.com/2024-MindSpore-1/Code7/tree/main/convmixer

CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network

https://github.com/2024-MindSpore-1/Code7/tree/main/convnext

InceptionNeXt: When Inception Meets ConvNeXt

https://github.com/2anchao/VovJpu

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

https://github.com/2manslkh/korean-food-classification-flask

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/3P2S/arcface

Deep Residual Learning for Image Recognition

https://github.com/3dlg-hcvc/M3DRef-CLIP

PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation

https://github.com/3dperceptionlab/therobotrix

UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation

https://github.com/44aayush/Denoising-and-Enhancement

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/461054993/SDCN

Deep Clustering for Unsupervised Learning of Visual Features

https://github.com/4uiiurz1/pytorch-deform-conv-v2

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/4uiiurz1/pytorch-nested-unet

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/583748495/psift

PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation

https://github.com/5m0k3/gwd-efficientdet-pytorch

Dynamic Scale Training for Object Detection

https://github.com/5m0k3/gwd-yolov5-pytorch

Dynamic Scale Training for Object Detection

https://github.com/632652101/CGNet-Pd

CGNet: A Light-weight Context Guided Network for Semantic Segmentation

https://github.com/632652101/VisualizeCNN-Pd

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

https://github.com/920232796/setr-pytorch

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

https://github.com/96jonesa/StyleGan2-Colab-Demo

Efficient Attention: Attention with Linear Complexities

https://github.com/9p15p/frtm-vos

Learning Fast and Robust Target Models for Video Object Segmentation

https://github.com/AD2605/Action-Recognition

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AFeng-x/Draw-and-Understand

Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos

https://github.com/AI-Chen/ColorMapGAN

ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

https://github.com/AI-Guru/pointcloud_experiments

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/AICIP-UTK/spacenet-round3

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AKASH2907/bird-species-classification

Mask R-CNN

https://github.com/AKASH2907/bird_species_classification

Mask R-CNN

https://github.com/ALBERT-Inc/blog_ssap

SSAP: Single-Shot Instance Segmentation With Affinity Pyramid

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.