Papers with Code 아카이브

Semantic Segmentation

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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/ChenJoya/sampling-free

공식 코드

Is Heuristic Sampling Necessary in Training Deep Object Detectors?

https://github.com/ChenLiu-1996/CUTS

공식 코드

CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation

https://github.com/Chenglin-Yang/LESA

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Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms

https://github.com/Chenglin-Yang/MOS_Meticulous-Object-Segmentation

공식 코드

Meticulous Object Segmentation

https://github.com/Chenguoz/PointSCNet

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PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling

https://github.com/ChengyueGongR/PatchVisionTransformer

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Vision Transformers with Patch Diversification

https://github.com/Chenyang-Lu/semantic-foreground-inpainting

공식 코드

Semantic Foreground Inpainting from Weak Supervision

https://github.com/ChenyangLEI/deep-video-prior

공식 코드

Deep Video Prior for Video Consistency and Propagation

https://github.com/ChenyunWu/PhraseCutDataset

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PhraseCut: Language-based Image Segmentation in the Wild

https://github.com/China-LiuXiaopeng/BraTS-DMFNet

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3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI

https://github.com/ChipsGuardian/DualConv

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DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks

https://github.com/ChrisMats/CSAW-S

공식 코드

Adding Seemingly Uninformative Labels Helps in Low Data Regimes

https://github.com/ChrisMats/seemingly_uninformative_labels

공식 코드

Adding Seemingly Uninformative Labels Helps in Low Data Regimes

https://github.com/ChristophReich1996/Cell-DETR

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Attention-Based Transformers for Instance Segmentation of Cells in Microstructures

https://github.com/Chuanhai/Mask-Editor

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Mask Editor : an Image Annotation Tool for Image Segmentation Tasks

https://github.com/ChuanxinSong/SRRM

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SRRM: Semantic Region Relation Model for Indoor Scene Recognition

https://github.com/ChunTao1999/SSDI

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Towards Image Semantics and Syntax Sequence Learning

https://github.com/ChunaraLab/FairDCL-mitigating-urban-rural-disparity

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Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery

https://github.com/ChunpingQiu/Human-settlement-extent-detection-from-Sentinel-2-images-via-fully-convolutional-neural-networks-

공식 코드

A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks

https://github.com/CognitiveModeling/Loci-Segmented

공식 코드

Loci-Segmented: Improving Scene Segmentation Learning

https://github.com/Cold-Winter/vqs

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VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation

https://github.com/Colin97/PartSLIP_page

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PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models

https://github.com/CompVis/mask

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[MASK] is All You Need

https://github.com/Confusezius/unet-lits-2d-pipeline

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Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function

https://github.com/CraftJarvis/ROCKET-1

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ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context Prompting

https://github.com/CrashedBboy/SGGIC

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Semantics-Guided Generative Image Compression

https://github.com/CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix

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Segmentation of skin lesions and their attributes using Generative Adversarial Networks

https://github.com/CuiRuikai/SAM-SOD

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Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection

https://github.com/CuriousAI/mean-teacher

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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/CurryYuan/InstanceRefer

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InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring

https://github.com/Cuzyoung/CoDA

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CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning

https://github.com/Cybonic/DL_vineyard_segmentation_study

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Multispectral Vineyard Segmentation: A Deep Learning approach

https://github.com/DBDXSS/Window-Normalization

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Window Normalization: Enhancing Point Cloud Understanding by Unifying Inconsistent Point Densities

https://github.com/DIAGNijmegen/neural-odes-segmentation

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Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

https://github.com/DIAGNijmegen/prostateMR_3D-CAD-csPCa

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Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI $-$Should Different Clinical Objectives Mandate Different Loss Functions?

https://github.com/DIAL-RPI/COVID19-ICUPrediction

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Integrative Analysis for COVID-19 Patient Outcome Prediction

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