Papers with Code 아카이브

2D Semantic Segmentation

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하위 태스크

17

평가표

11

보존된 평가 기록

42

코드

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

https://github.com/baegwangbin/surface_normal_uncertainty

공식 코드

Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

https://github.com/bahramjafrasteh/melage

공식 코드

MELAGE: A purely python based Neuroimaging software (Neonatal)

https://github.com/bahramjafrasteh/mga-net

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Mask-Guided Attention U-Net for Enhanced Neonatal Brain Extraction and Image Preprocessing

https://github.com/baiwenjia/CIMAS

공식 코드

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

https://github.com/bala93/marginloss

공식 코드

Calibrating Segmentation Networks with Margin-based Label Smoothing

https://github.com/balbasty/nitorch

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An MRF-UNet Product of Experts for Image Segmentation

https://github.com/baldassarreFe/ws-vrd

공식 코드

Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks

https://github.com/bang123-box/cfe

공식 코드

Focus on the Whole Character: Discriminative Character Modeling for Scene Text Recognition

https://github.com/bangwayne/verse

공식 코드

VerSe: Integrating Multiple Queries as Prompts for Versatile Cardiac MRI Segmentation

https://github.com/baobao0926/asp-vmunet

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ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation

https://github.com/baudm/parseq

공식 코드

Scene Text Recognition with Permuted Autoregressive Sequence Models

https://github.com/baumgach/PHiSeg-code

공식 코드

PHiSeg: Capturing Uncertainty in Medical Image Segmentation

https://github.com/baumgach/acdc_segmenter

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An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation

https://github.com/bcv-uniandes/grasp

공식 코드

Pixel-Wise Recognition for Holistic Surgical Scene Understanding

https://github.com/bcv-uniandes/matis

공식 코드

Pixel-Wise Recognition for Holistic Surgical Scene Understanding

https://github.com/bcv-uniandes/tapir

공식 코드

Towards Holistic Surgical Scene Understanding

https://github.com/beginner-cjh/medvkan

공식 코드

MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation

https://github.com/beileicui/ms-tfal

공식 코드

Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation

https://github.com/beltransen/velo2cam_calibration

공식 코드

Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups

https://github.com/beltransen/velo2cam_gazebo

공식 코드

Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups

https://github.com/benjamintowle/seqsam

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SeqSAM: Autoregressive Multiple Hypothesis Prediction for Medical Image Segmentation using SAM

https://github.com/benjamintowle/simsam

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SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction

https://github.com/benolmbrt/wcp_miccai

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Robust Conformal Volume Estimation in 3D Medical Images

https://github.com/berkeley-hipie/hipie

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Hierarchical Open-vocabulary Universal Image Segmentation

https://github.com/berkels/msiplib

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A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation

https://github.com/bermanmaxim/LovaszSoftmax

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The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

https://github.com/bermanmaxim/superpixPool

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Efficient semantic image segmentation with superpixel pooling

https://github.com/bev-gorry/underloc

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Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments

https://github.com/bgu-cs-vil/wtconv

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Wavelet Convolutions for Large Receptive Fields

https://github.com/bhanukaManesha/embarrassingly-simple-classifier-comparison

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An embarrassingly simple comparison of machine learning algorithms for indoor scene classification

https://github.com/biclab/spike2former

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Spike2Former: Efficient Spiking Transformer for High-performance Image Segmentation

https://github.com/bilel-bj/unsupervised-domain-adaptation-gan

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Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

https://github.com/billhhh/ShaSpec

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Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling

https://github.com/binalab/rescuenet-a-high-resolution-post-disaster-uav-dataset-for-semantic-segmentation

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RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

https://github.com/bingogome/samm

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SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM

https://github.com/binycn/dcpa

공식 코드

Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation

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