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/NUST-Machine-Intelligence-Laboratory/HFAN

공식 코드

Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation

https://github.com/NUST-Machine-Intelligence-Laboratory/SMCP

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Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation

https://github.com/NUST-Machine-Intelligence-Laboratory/nsrom

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Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

https://github.com/NVIDIA/DeepLearningExamples

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Optimized U-Net for Brain Tumor Segmentation

https://github.com/NVIDIA/partialconv

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Partial Convolution based Padding

https://github.com/NVIDIA/pix2pixHD

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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/NVIDIA/semantic-segmentation

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Hierarchical Multi-Scale Attention for Semantic Segmentation

https://github.com/NVIDIA/vid2vid

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Video-to-Video Synthesis

https://github.com/NVlabs/GroupViT

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GroupViT: Semantic Segmentation Emerges from Text Supervision

https://github.com/NVlabs/SENSE

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SENSE: a Shared Encoder Network for Scene-flow Estimation

https://github.com/NVlabs/STL

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Fully Attentional Networks with Self-emerging Token Labeling

https://github.com/NVlabs/SegFormer

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SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/NZWANG/CSC-Unet

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CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

https://github.com/Na-Z/PS-2Net

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PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation

https://github.com/Na-Z/attMPTI

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Few-shot 3D Point Cloud Semantic Segmentation

https://github.com/Nathan-UW/OpenGF

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OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World

https://github.com/NathanUA/BASNet

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Boundary-Aware Segmentation Network for Mobile and Web Applications

https://github.com/NazirNayal8/RbA

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RbA: Segmenting Unknown Regions Rejected by All

https://github.com/Negin-Ghamsarian/AdaptNet-MICCAI2021

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LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos

https://github.com/NesterukSergey/segmentation_image_augmentation

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Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case

https://github.com/NeurAI-Lab/UniNet

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UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks

https://github.com/Nexuslkl/Swin_MIL

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Transformer based multiple instance learning for weakly supervised histopathology image segmentation

https://github.com/Nguyendat-bit/U-net

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ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data

https://github.com/NiFangBaAGe/DATTT

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Depth-aware Test-Time Training for Zero-shot Video Object Segmentation

https://github.com/Nightmare-n/UniPAD

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UniPAD: A Universal Pre-training Paradigm for Autonomous Driving

https://github.com/NikooMoradi/HNTSMRG24_team_TUMOR

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Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy

https://github.com/Nisha1803/Methodology_Endosomal_Escape

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Stochastic Model of siRNA Endosomal Escape Mediated by Fusogenic Peptides in OVCAR-3

https://github.com/Nithin-GK/UniteandConquer

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Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models

https://github.com/NoelShin/PixelPick

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All you need are a few pixels: semantic segmentation with PixelPick

https://github.com/NoelShin/reco

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ReCo: Retrieve and Co-segment for Zero-shot Transfer

https://github.com/NormXU/Layout2Graph

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PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis

https://github.com/NoviceMAn-prog/MSRF-Net

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MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation

https://github.com/OATML/Non-Parametric-Transformers

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Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning

https://github.com/OFA-Sys/ONE-PEACE

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ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities

https://github.com/OFRIN/PuzzleCAM

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Puzzle-CAM: Improved localization via matching partial and full features

https://github.com/OIPLab-DUT/CoNet

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Accurate RGB-D Salient Object Detection via Collaborative Learning

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