Papers with Code 아카이브

Semantic Segmentation

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150

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코드

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

https://github.com/mahmoudnafifi/WB_color_augmenter

공식 코드

What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance

https://github.com/majid9418/eresu-net

공식 코드

Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning

https://github.com/majy-yuji/st-usleepnet

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ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Staging

https://github.com/makamoa/hyplex

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Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders

https://github.com/makbari7/DSSLIC

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DSSLIC: Deep Semantic Segmentation-based Layered Image Compression

https://github.com/malga-vision/co-semdepth

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Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Images

https://github.com/malidib/Craters_MaskRCNN

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Automated crater shape retrieval using weakly-supervised deep learning

https://github.com/maloadba/mgenseg_2d

공식 코드

M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision

https://github.com/mamrehn/interactive_image_segmentation_evaluation

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A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems

https://github.com/mancinimassimiliano/adagraph

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Towards Recognizing New Semantic Concepts in New Visual Domains

https://github.com/manem-lab/lung-ddpm

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Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis

https://github.com/manmanjun/undoing_uda

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Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation

https://github.com/manuelknott/banana-defect-segmentation

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Weakly supervised image segmentation for defect-based grading of fresh produce

https://github.com/manugar/imagestyletransfer

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Neural Style Transfer and Unpaired Image-to-Image Translation to deal with the Domain Shift Problem on Spheroid Segmentation

https://github.com/maoyunyao/joint

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Joint Inductive and Transductive Learning for Video Object Segmentation

https://github.com/mapillary/inplace_abn

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In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

https://github.com/mapleandfire/li-convs

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Dilated Convolutions with Lateral Inhibitions for Semantic Image Segmentation

https://github.com/marcdcfischer/punet

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Prompt Tuning for Parameter-efficient Medical Image Segmentation

https://github.com/marco-garosi/COPS

공식 코드

3D Part Segmentation via Geometric Aggregation of 2D Visual Features

https://github.com/marcofiorucci/ot-4-change-detection

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Optimal Transport for Change Detection on LiDAR Point Clouds

https://github.com/marcus-nordstrom/optimal-solutions-to-accuracy-and-dice

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On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice

https://github.com/mariannerakic/tyche

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Tyche: Stochastic In-Context Learning for Medical Image Segmentation

https://github.com/marianocabezas/challenges2018

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Survival prediction using ensemble tumor segmentation and transfer learning

https://github.com/marinbenc/medical-polar-training

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Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images

https://github.com/mariodoebler/test-time-adaptation

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Introducing Intermediate Domains for Effective Self-Training during Test-Time

https://github.com/marionacaros/3d-object-segmentation

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Object Segmentation of Cluttered Airborne LiDAR Point Clouds

https://github.com/marionacaros/barlow-twins-for-sem-seg

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Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data

https://github.com/marios2019/CSN

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Cross-Shape Attention for Part Segmentation of 3D Point Clouds

https://github.com/mark12ding/sam2long

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SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree

https://github.com/marksgraham/transformer-ood

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Transformer-based out-of-distribution detection for clinically safe segmentation

https://github.com/markusschoen/mgnet

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MGNet: Monocular Geometric Scene Understanding for Autonomous Driving

https://github.com/markusschoen/rt-k-net

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RT-K-Net: Revisiting K-Net for Real-Time Panoptic Segmentation

https://github.com/martianxiu/als_pretraining

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Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream Assessment

https://github.com/martianxiu/diffusionunit

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Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation

https://github.com/martin-bayon/tednet

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TEDNet: Twin Encoder Decoder Neural Network for 2D Camera and LiDAR Road Detection

https://github.com/martinferianc/ComBiNet

공식 코드

ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

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