Papers with Code 아카이브

2D Semantic Segmentation

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하위 태스크

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평가표

11

보존된 평가 기록

42

코드

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

https://github.com/ashesham/tv3s

공식 코드

Exploiting Temporal State Space Sharing for Video Semantic Segmentation

https://github.com/ashiq24/scale_equivarinat_fourier_layer

공식 코드

Truly Scale-Equivariant Deep Nets with Fourier Layers

https://github.com/asif-hanif/vafa

공식 코드

Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation

https://github.com/askerlee/segtran

공식 코드

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

https://github.com/aspirinone/catr.github.io

공식 코드

CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation

https://github.com/asterisci/language-assisted-3d

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Language-Assisted 3D Feature Learning for Semantic Scene Understanding

https://github.com/asterisci/point-gcc

공식 코드

Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast

https://github.com/astlian9/sam_support

공식 코드

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting

https://github.com/astonzhang/Parameterization-of-Hypercomplex-Multiplications

공식 코드

Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters

https://github.com/astra-vision/densemtl

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DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning

https://github.com/aswahd/sam2radiology

공식 코드

Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts

https://github.com/aswahd/samradiology

공식 코드

Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts

https://github.com/atapour/temporal-depth-segmentation

공식 코드

Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach

https://github.com/atr-dbi/scanqa

공식 코드

ScanQA: 3D Question Answering for Spatial Scene Understanding

https://github.com/atrovast/thgs

공식 코드

Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs

https://github.com/att100/ucloudnet

공식 코드

UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation

https://github.com/audiollms/audiobench

공식 코드

AudioBench: A Universal Benchmark for Audio Large Language Models

https://github.com/aurora-hao/hct

공식 코드

Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

https://github.com/auroua/hrstnet

공식 코드

High-Resolution Swin Transformer for Automatic Medical Image Segmentation

https://github.com/autogluon/autogluon

공식 코드

Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model

https://github.com/autonomousvision/kitti360labeltool

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KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D

https://github.com/autonomousvision/kitti360scripts

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KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D

https://github.com/avc2-uestc/qmedsam

공식 코드

Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment

https://github.com/avm-debatr/bagganhq

공식 코드

Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

https://github.com/avm-debatr/ganecdotes

공식 코드

Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

https://github.com/awadelrahman/medai

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Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation

https://github.com/axi404/pmt

공식 코드

PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation

https://github.com/aymanbegh/hybrid-gcnn-cnn

공식 코드

A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection Inference

https://github.com/ayumiymk/DiG

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Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition

https://github.com/ayushroy2001/gru-net

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GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation

https://github.com/azadef/ynet

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Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation

https://github.com/azhuantou/aiscene

공식 코드

Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentation

https://github.com/azuma164/zodi

공식 코드

ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer

https://github.com/baai-dcai/segvol

공식 코드

SegVol: Universal and Interactive Volumetric Medical Image Segmentation

https://github.com/baaivision/painter

공식 코드

SegGPT: Segmenting Everything In Context

https://github.com/backpropagator/langdaug

공식 코드

LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation

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