Papers with Code 아카이브

Semantic Segmentation

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코드

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

https://github.com/vlbthambawita/pyra-pytorch

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Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus

https://github.com/vlbthambawita/singan-seg-polyp

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SinGAN-Seg: Synthetic training data generation for medical image segmentation

https://github.com/vobecant/DriveAndSegment

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Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation

https://github.com/voldemortX/DST-CBC

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DMT: Dynamic Mutual Training for Semi-Supervised Learning

https://github.com/volvo-cars/fb-ssem-dataset

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F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving

https://github.com/vonguyenleduy/dnn_representation_selective_inference

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Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference

https://github.com/vonguyenleduy/dnn_segmentation_selective_inference

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Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference

https://github.com/voreille/hecktor

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Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

https://github.com/vpulab/Semantic-Aware-Scene-Recognition

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Semantic-Aware Scene Recognition

https://github.com/vpulab/med-sam-brain

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GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI Data

https://github.com/vpulab/ovam

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Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models

https://github.com/vpulab/soft-labels-ss

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Soft labelling for semantic segmentation: Bringing coherence to label down-sampling

https://github.com/vpulab/synthmanticlidar

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SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging

https://github.com/vri-ufpr/dacov2022

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DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem

https://github.com/vri-ufpr/sparkinthedarklars2021

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Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation

https://github.com/vribeiro1/skin-lesion-segmentation-agreement

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Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation

https://github.com/vsydorskyy/hubmap_2022_htt_solution

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Semi-Supervised Segmentation of Functional Tissue Units at the Cellular Level

https://github.com/vujas-eteph/lazyxmem

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Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentation

https://github.com/vvarga90/gnn_annot

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Fast Interactive Video Object Segmentation with Graph Neural Networks

https://github.com/vvjia/sam-fnet

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SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection

https://github.com/w-zx-y/sm-ppm

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Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation

https://github.com/w1oves/dtp

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Disentangle then Parse:Night-time Semantic Segmentation with Illumination Disentanglement

https://github.com/w1oves/rein

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Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation

https://github.com/w1zheng/dknet

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3D Instances as 1D Kernels

https://github.com/w1zheng/insteacher3d

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Instance Consistency Regularization for Semi-Supervised 3D Instance Segmentation

https://github.com/walbouss/gem

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Grounding Everything: Emerging Localization Properties in Vision-Language Transformers

https://github.com/walking-shadow/Official_Remote_Sensing_Mamba

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RS-Mamba for Large Remote Sensing Image Dense Prediction

https://github.com/walking-shadow/official_tssun

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Temporal-Spectral-Spatial Unified Remote Sensing Dense Prediction

https://github.com/wall-facer-liuyu/bo-qsa

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Improving Object-centric Learning with Query Optimization

https://github.com/waltersimoncini/fungivision

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No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations

https://github.com/wang-chaoyang/semflow

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SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow

https://github.com/wangangcheng/lora-pt

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LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and Vectors

https://github.com/wangchi95/active-boundary-loss

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Active Boundary Loss for Semantic Segmentation

https://github.com/wangck20/globalmamba

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GlobalMamba: Global Image Serialization for Vision Mamba

https://github.com/wangck20/v2m

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V2M: Visual 2-Dimensional Mamba for Image Representation Learning

https://github.com/wangf3014/cp2

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CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation

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