Papers with Code 아카이브

Semantic Segmentation

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하위 태스크

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150

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코드

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

https://github.com/Yochengliu/DensePoint

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DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing

https://github.com/Yochengliu/Relation-Shape-CNN

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Relation-Shape Convolutional Neural Network for Point Cloud Analysis

https://github.com/YorkUCVIL/Static-Dynamic-Interpretability

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A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information

https://github.com/YtongXie/CoTr

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CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

https://github.com/YuHengsss/Trident

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Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation

https://github.com/YuLiu-LY/BO-QSA

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Improving Object-centric Learning with Query Optimization

https://github.com/YuYin1/DA-GAN

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Dual-Attention GAN for Large-Pose Face Frontalization

https://github.com/YudeWang/SEAM

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Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

https://github.com/YudeWang/SSENet-pytorch

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Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation

https://github.com/YuvalNirkin/fsgan

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FSGAN: Subject Agnostic Face Swapping and Reenactment

https://github.com/YuvalNirkin/hyperseg

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HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation

https://github.com/Yux1angJi/DIFF

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Diffusion Features to Bridge Domain Gap for Semantic Segmentation

https://github.com/Yuxuan-Wen/Sim2real-Guidewire-Seg

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Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy

https://github.com/Z-Zheng/ChangeStar

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Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery

https://github.com/Z-Zheng/FarSeg

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Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

https://github.com/ZENGXH/DMM_Net

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DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation

https://github.com/ZHKKKe/PixelSSL

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Guided Collaborative Training for Pixel-wise Semi-Supervised Learning

https://github.com/ZJUGiveLab/UNet-Version

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UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation

https://github.com/ZJULearning/MaxSquareLoss

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Domain Adaptation for Semantic Segmentation with Maximum Squares Loss

https://github.com/ZJULearning/RMI

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Region Mutual Information Loss for Semantic Segmentation

https://github.com/ZRandomize/MatrixVT

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MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception

https://github.com/Zch0414/P2SAM

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Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation

https://github.com/ZechengLi19/CIM

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Complete Instances Mining for Weakly Supervised Instance Segmentation

https://github.com/ZerojumpLine/OverfittingUnderClassImbalance

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Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation

https://github.com/ZhangLi-CS/GCP_Optimization

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What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective

https://github.com/ZhgLiu/GDD

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Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense Prediction

https://github.com/ZhihuaLiuEd/SoTA-Brain-Tumor-Segmentation

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Deep Learning Based Brain Tumor Segmentation: A Survey

https://github.com/ZhihuaLiuEd/canetbrats

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CANet: Context Aware Network for 3D Brain Glioma Segmentation

https://github.com/ZhilingYan/Biomedical-SAM-2

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Biomedical SAM 2: Segment Anything in Biomedical Images and Videos

https://github.com/ZhouZijie77/SegLocNet

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SegLocNet: Multimodal Localization Network for Autonomous Driving via Bird's-Eye-View Segmentation

https://github.com/Zhourobotics/Query-3DGS-LLM

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Query3D: LLM-Powered Open-Vocabulary Scene Segmentation with Language Embedded 3D Gaussian

https://github.com/ZiqinZhou66/ZegCLIP

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ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation

https://github.com/Zzh-tju/CIoU

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Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation

https://github.com/a-nau/synthetic-dataset-generation

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Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics

https://github.com/a-ngj/image-segmentation-cli

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Visual Context-Aware Person Fall Detection

https://github.com/a2amir/sfs-a68

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SFS-A68: a dataset for the segmentation of space functions in apartment buildings

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