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Semantic Segmentation

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

https://github.com/pinxueguo/x-prompt

공식 코드

X-Prompt: Multi-modal Visual Prompt for Video Object Segmentation

https://github.com/piooip/ud-mamba

공식 코드

UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation

https://github.com/pit30m/pit30m

공식 코드

Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars

https://github.com/pjjajal/efficientttms

공식 코드

Token Turing Machines are Efficient Vision Models

https://github.com/pjlab-adg/3dtrans

공식 코드

SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations

https://github.com/pjlab-adg/pcseg

공식 코드

UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase

https://github.com/pjlallen/calibnet

공식 코드

CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation

https://github.com/pjlallen/osformer

공식 코드

OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers

https://github.com/pjlallen/udun

공식 코드

Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation

https://github.com/pjsjongsung/PUMBA

공식 코드

Skull stripping with purely synthetic data

https://github.com/pjsjongsung/evac

공식 코드

EVAC+: Multi-scale V-net with Deep Feature CRF Layers for Brain Extraction

https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration

공식 코드

Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration

https://github.com/pkyong95/PCVOS

공식 코드

Per-Clip Video Object Segmentation

https://github.com/pl23k/icehrnet

공식 코드

An easy zero-shot learning combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy

https://github.com/placeforyiming/divide-and-merge-lidar-panoptic-cluster

공식 코드

A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation

https://github.com/placeforyiming/iccvw21-lidar-panoptic-segmentation-tradicv-survey-of-point-cloud-cluster

공식 코드

A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation

https://github.com/placeforyiming/iros21-fidnet-semantickitti

공식 코드

FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding

https://github.com/pldelisle/deepNormalize

공식 코드

Realistic Image Normalization for Multi-Domain Segmentation

https://github.com/plusmultiply/mprm

공식 코드

Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds

https://github.com/pmeletis/hierarchical-semantic-segmentation

공식 코드

Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation

https://github.com/pnnl/SAMIAm

공식 코드

SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation

https://github.com/pnnl/pychip_gui

공식 코드

Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

https://github.com/pointcept/pointtransformerv3

공식 코드

Point Transformer V3: Simpler, Faster, Stronger

https://github.com/ponimatkin/ssl-vos

공식 코드

A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation

https://github.com/poppinace/fade

공식 코드

FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures

https://github.com/poppinace/sapa

공식 코드

SAPA: Similarity-Aware Point Affiliation for Feature Upsampling

https://github.com/postBG/DTA.pytorch

공식 코드

Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation

https://github.com/potsui99/unirgb-ir

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UniRGB-IR: A Unified Framework for RGB-Infrared Semantic Tasks via Adapter Tuning

https://github.com/ppwwyyxx/tensorpack

공식 코드

Learning Strict Identity Mappings in Deep Residual Networks

https://github.com/pqhieu/JSIS3D

공식 코드

JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields

https://github.com/prachigarg23/mdil-ss

공식 코드

Multi-Domain Incremental Learning for Semantic Segmentation

https://github.com/pranavphoenix/WaveMix

공식 코드

WaveMix: A Resource-efficient Neural Network for Image Analysis

https://github.com/pranavsinghps1/s4mi

공식 코드

Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification

https://github.com/prantikbubun/beyond-pixels

공식 코드

Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier

https://github.com/prassanna-ravishankar/slither

공식 코드

Unstructured Road Segmentation using Hypercolumn based Random Forests of Local experts

https://github.com/prbonn/3diss

공식 코드

Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving

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