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/maudzung/SFA3D

공식 코드

Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment

https://github.com/maunzzz/cross-season-segmentation

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A Cross-Season Correspondence Dataset for Robust Semantic Segmentation

https://github.com/mawanqin2002/stacom2022ma

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Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels

https://github.com/max810/XMem2

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XMem++: Production-level Video Segmentation From Few Annotated Frames

https://github.com/maxfrei750/FibeR-CNN

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FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis

https://github.com/maxfrei750/synthPIC4Python

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FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis

https://github.com/maxiao0234/ttga

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Test-Time Generative Augmentation for Medical Image Segmentation

https://github.com/maximiliancoenen/consinstancy

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ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles

https://github.com/maxjiang93/DDSL

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DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals

https://github.com/maxjiang93/ugscnn

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Spherical CNNs on Unstructured Grids

https://github.com/maxproto/iba

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Influencer Backdoor Attack on Semantic Segmentation

https://github.com/maxwell0027/lefed

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Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation

https://github.com/maycbj/dit

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DiT: Efficient Vision Transformers with Dynamic Token Routing

https://github.com/mazurowski-lab/SlicerSegmentHumanBody

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Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

https://github.com/mazurowski-lab/finetune-sam

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How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model

https://github.com/mazurowski-lab/sam-texturalconfusion-metrics

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Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects

https://github.com/mazurowski-lab/segment-anything-medical

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Segment Anything Model for Medical Image Analysis: an Experimental Study

https://github.com/mazurowski-lab/segment-anything-medical-evaluation

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Segment Anything Model for Medical Image Analysis: an Experimental Study

https://github.com/mazurowski-lab/single-image-test-time-adaptation

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Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation

https://github.com/mazurowski-lab/slm-sam2

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Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

https://github.com/mbjurca/RT_GS2

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RT-GS2: Real-Time Generalizable Semantic Segmentation for 3D Gaussian Representations of Radiance Fields

https://github.com/mbzuai-oryx/groundingLMM

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GLaMM: Pixel Grounding Large Multimodal Model

https://github.com/mbzuai-oryx/videomolmo

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VideoMolmo: Spatio-Temporal Grounding Meets Pointing

https://github.com/mc-e/transcl

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TransCL: Transformer Makes Strong and Flexible Compressive Learning

https://github.com/mc-lan/proxyclip

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ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

https://github.com/mc-lan/smooseg

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SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation

https://github.com/mc-lan/text4seg

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Text4Seg: Reimagining Image Segmentation as Text Generation

https://github.com/mcahny/vps

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Video Panoptic Segmentation

https://github.com/mcbuehler/Seg2Eye

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Content-Consistent Generation of Realistic Eyes with Style

https://github.com/mcg-nju/link

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LinK: Linear Kernel for LiDAR-based 3D Perception

https://github.com/mcgregorwwww/uctransnet

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UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer

https://github.com/mckellwoodland/dimen_reduce_mahal

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Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation

https://github.com/md-mohaiminul/trans4mer

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Efficient Movie Scene Detection using State-Space Transformers

https://github.com/mdca-loss/mdca-calibration

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A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration

https://github.com/mdciri/3D-augmentation-techniques

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What is the best data augmentation for 3D brain tumor segmentation?

https://github.com/mdciri/Vox2Vox

공식 코드

Vox2Vox: 3D-GAN for Brain Tumour Segmentation

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