Papers with Code 아카이브

2D Semantic Segmentation

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전체 기법전체 데이터셋

하위 태스크

17

평가표

11

보존된 평가 기록

42

코드

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

https://github.com/wzh0120/sam2-unet

공식 코드

SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation

https://github.com/wzx99/clipocr

공식 코드

Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition

https://github.com/wzzheng/stag

공식 코드

Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model

https://github.com/xahidbuffon/srdrm

공식 코드

Underwater Image Super-Resolution using Deep Residual Multipliers

https://github.com/xamyzhao/brainstorm

공식 코드

Data augmentation using learned transformations for one-shot medical image segmentation

https://github.com/xavijuanola/vssl_eval

공식 코드

A Critical Assessment of Visual Sound Source Localization Models Including Negative Audio

https://github.com/xavysp/teed

공식 코드

Tiny and Efficient Model for the Edge Detection Generalization

https://github.com/xcppy/ws-sgg

공식 코드

Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph Generation

https://github.com/xdxie/WAS_WordArt-Segmentation

공식 코드

WAS: Dataset and Methods for Artistic Text Segmentation

https://github.com/xdxie/wordart

공식 코드

Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition

https://github.com/xfey/mctbench

공식 코드

MCTBench: Multimodal Cognition towards Text-Rich Visual Scenes Benchmark

https://github.com/xharlie/btcdet

공식 코드

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

https://github.com/xheon/panoptic-reconstruction

공식 코드

Panoptic 3D Scene Reconstruction From a Single RGB Image

https://github.com/xhu248/autosam

공식 코드

How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images

https://github.com/xhu248/cisfa_da

공식 코드

Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation

https://github.com/xhu248/cond_ddpm_wsss

공식 코드

Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation

https://github.com/xhu248/semi_cotrast_seg

공식 코드

Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation

https://github.com/xia-zs/SSLBDA

공식 코드

Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets

https://github.com/xiangxu-0103/superflow

공식 코드

SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data Pretraining

https://github.com/xianlin7/apformer

공식 코드

The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning

https://github.com/xianlin7/batformer

공식 코드

BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation

https://github.com/xianlin7/c2ftrans

공식 코드

BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation

https://github.com/xianlin7/convformer

공식 코드

ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation

https://github.com/xianlin7/samus

공식 코드

Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting

https://github.com/xiaoaoran/FPS-Net

공식 코드

FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation

https://github.com/xiaofang007/cto

공식 코드

Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation

https://github.com/xiaofang007/phnet

공식 코드

Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation

https://github.com/xiaofei05/tsst

공식 코드

Transductive Learning for Unsupervised Text Style Transfer

https://github.com/xiaohao-xu/ambiguity-in-space

공식 코드

Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth Ambiguity

https://github.com/xiaoqi-zhao-dlut/msnet

공식 코드

M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

https://github.com/xiaoqi-zhao-dlut/multi-source-aps-zvos

공식 코드

Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation

https://github.com/xiaoqinzhe/vrdetection

공식 코드

AVR: Attention based Salient Visual Relationship Detection

https://github.com/xiaoyao3302/2bdenoise

공식 코드

Clean Label Disentangling for Medical Image Segmentation with Noisy Labels

https://github.com/xieenze/TextSR

공식 코드

TextSR: Content-Aware Text Super-Resolution Guided by Recognition

https://github.com/xilin1991/CluterNet

공식 코드

Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering

https://github.com/xilin1991/IMCNet

공식 코드

Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation

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