Papers with Code 아카이브

Semantic Segmentation

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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/minghanli/mdqe_cvpr2023

공식 코드

MDQE: Mining Discriminative Query Embeddings to Segment Occluded Instances on Challenging Videos

https://github.com/minghanli/univs

공식 코드

UniVS: Unified and Universal Video Segmentation with Prompts as Queries

https://github.com/minghanqin/LangSplat

공식 코드

LangSplat: 3D Language Gaussian Splatting

https://github.com/mingliangzhang2018/PGDP

공식 코드

Plane Geometry Diagram Parsing

https://github.com/mingxuangu/few-shot-uda

공식 코드

Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation

https://github.com/minhquanlecs/maskdiff

공식 코드

MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

https://github.com/minimel/medsamweakfewshotpromptautomation

공식 코드

Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision

https://github.com/minimel/stochasticbatchal

공식 코드

Active learning for medical image segmentation with stochastic batches

https://github.com/minqili/viton-drr-main

공식 코드

VITON-DRR: Details Retention Virtual Try-on via Non-rigid Registration

https://github.com/minsu-kim320/Cross-Domain-Grouping-and-Alignment-for-Domain-Adaptive-Semantic-Segmentation

공식 코드

Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation

https://github.com/miquel-espinosa/no-time-to-train

공식 코드

No time to train! Training-Free Reference-Based Instance Segmentation

https://github.com/miquelmarti/DenseFixMatch

공식 코드

Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks

https://github.com/miranheo/genvis

공식 코드

A Generalized Framework for Video Instance Segmentation

https://github.com/mirthAI/CIS-UNet

공식 코드

CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention

https://github.com/mirthai/csa-net

공식 코드

A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention

https://github.com/missu-hh/exact

공식 코드

Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation

https://github.com/mistlab/dovesei

공식 코드

Dynamic Open Vocabulary Enhanced Safe-landing with Intelligence (DOVESEI)

https://github.com/mit-han-lab/bevfusion

공식 코드

BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

https://github.com/mit-han-lab/efficientvit

공식 코드

EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

https://github.com/mit-han-lab/pvcnn

공식 코드

Point-Voxel CNN for Efficient 3D Deep Learning

https://github.com/mit-han-lab/spvnas

공식 코드

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/mit-tesse/dsg-rl

공식 코드

Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks

https://github.com/mivanovska/tomatodiff

공식 코드

TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models

https://github.com/mj129/cipa

공식 코드

Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images

https://github.com/mkang315/asf-yolo

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ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation

https://github.com/mkang315/mctseg

공식 코드

A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

https://github.com/mkg1204/rhmnet-for-ssvos

공식 코드

Reliability-Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation

https://github.com/mkhangg/refined_panoptic_mapping

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Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots

https://github.com/mkimhi/RISE

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Robot Instance Segmentation with Few Annotations for Grasping

https://github.com/mkmenta/domain_adapt_segm

공식 코드

Learning to adapt class-specific features across domains for semantic segmentation

https://github.com/mkruuse/segmenting-turbulent-simulations-with-ensemble-learning

공식 코드

Segmentation of turbulent computational fluid dynamics simulations with unsupervised ensemble learning

https://github.com/mkuuwaujinga/leopart

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Self-Supervised Learning of Object Parts for Semantic Segmentation

https://github.com/ml-jku/MIM-Refiner

공식 코드

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations

https://github.com/ml-postech/active-label-correction

공식 코드

Active Label Correction for Semantic Segmentation with Foundation Models

https://github.com/ml-postech/adaptive-superpixel-for-active-learning-in-semantic-segmentation

공식 코드

Adaptive Superpixel for Active Learning in Semantic Segmentation

https://github.com/ml-research/deictic-segment-anything

공식 코드

DeiSAM: Segment Anything with Deictic Prompting

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