Papers with Code 아카이브

Semantic Segmentation

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하위 태스크

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평가표

150

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1,918

코드

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

https://github.com/moonsh/hm-hybrid-masking

공식 코드

HM: Hybrid Masking for Few-Shot Segmentation

https://github.com/moose-eccv22/moose_eccv2022

공식 코드

Probing Contextual Diversity for Dense Out-of-Distribution Detection

https://github.com/morancyc/usnet

공식 코드

Fast Road Segmentation via Uncertainty-aware Symmetric Network

https://github.com/morilabnu/bayesian_wss

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A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation

https://github.com/mostafaelaraby/bacs-continual-semantic-segmentation

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BACS: Background Aware Continual Semantic Segmentation

https://github.com/moucheng2017/Pay_Attention_To_Mistakes

공식 코드

Learning To Pay Attention To Mistakes

https://github.com/moucheng2017/emssl

공식 코드

Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation

https://github.com/moucheng2017/mismatchssl

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MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels

https://github.com/moured/document-graphics-digitization

공식 코드

Line Graphics Digitization: A Step Towards Full Automation

https://github.com/mpbarbato/unsupervised-segmentation-of-hyperspectral-remote-sensing-images-with-superpixels

공식 코드

Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels

https://github.com/mqinghe/midss

공식 코드

Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

https://github.com/mqinghe/synfoc

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Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation

https://github.com/mqinghe/ust-run

공식 코드

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation

https://github.com/mr-neko/jm3d

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Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation

https://github.com/mrazhou/seta

공식 코드

Scale Efficient Training for Large Datasets

https://github.com/mrblankness/lightm-unet

공식 코드

LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

https://github.com/mrcoee/automatic-label-error-detection

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Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification

https://github.com/mrebol/f2f-consistent-semantic-segmentation

공식 코드

Frame-To-Frame Consistent Semantic Segmentation

https://github.com/mrgiovanni/continuallearning

공식 코드

Continual Learning for Abdominal Multi-Organ and Tumor Segmentation

https://github.com/mrgiovanni/onlinelearning

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Embracing Massive Medical Data

https://github.com/mrgiovanni/suprem

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How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?

https://github.com/mrgiovanni/synthetictumors

공식 코드

Synthetic Tumors Make AI Segment Tumors Better

https://github.com/mrheffels/aerial-imagery-segmentation

공식 코드

Aerial Imagery Pixel-level Segmentation

https://github.com/mrinal054/instance_teeth_segmentation

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A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays

https://github.com/mrkshllr/bixt

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Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers

https://github.com/mrottmann/MetaSeg

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Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities

https://github.com/mseg-dataset/mseg-semantic

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MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

https://github.com/msiam/mmc-multiscalememory

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Multiscale Memory Comparator Transformer for Few-Shot Video Segmentation

https://github.com/msiam/tti_fsvos

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Temporal Transductive Inference for Few-Shot Video Object Segmentation

https://github.com/msracver/Deformable-ConvNets

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Deformable Convolutional Networks

https://github.com/msraig/pfcnn

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PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames

https://github.com/mszuyx/mosts

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A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications

https://github.com/mtangemann/motion_energy_segmentation

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Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli

https://github.com/mtneuro/mtneuro

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MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

https://github.com/mttr2021/MTTR

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End-to-End Referring Video Object Segmentation with Multimodal Transformers

https://github.com/mudit-adityaja/unsegmedgat

공식 코드

UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering

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