Papers with Code 아카이브

Semantic Segmentation

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하위 태스크

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평가표

150

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1,918

코드

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

https://github.com/liu-zihang/samsr

공식 코드

Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution

https://github.com/liuchen1997/rfaconv

공식 코드

RFAConv: Innovating Spatial Attention and Standard Convolutional Operation

https://github.com/liuff19/Semantic-Ray

공식 코드

Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention

https://github.com/liuhaozm/double-well-net

공식 코드

Double-well Net for Image Segmentation

https://github.com/liuquande/FedDG-ELCFS

공식 코드

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

https://github.com/liutinglt/CE2P

공식 코드

Devil in the Details: Towards Accurate Single and Multiple Human Parsing

https://github.com/liutingwed/sam-not-perfect

공식 코드

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications

https://github.com/liutongkun/comad

공식 코드

Component-aware anomaly detection framework for adjustable and logical industrial visual inspection

https://github.com/liutongkun/gfds

공식 코드

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models

https://github.com/liuwan0208/one-shot-extensive-data-augmentation

공식 코드

One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation

https://github.com/liuweide01/Cross-Image-Region-Mining-with-Region-Prototypical-Network-for-Weakly-Supervised-Segmentation

공식 코드

Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation

https://github.com/liuweide01/GQNet-Few-shot-segmentation

공식 코드

Few-Shot Segmentation with Global and Local Contrastive Learning

https://github.com/liuweide01/HBNC

공식 코드

Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation

https://github.com/liuxingbin/dbot

공식 코드

Exploring Target Representations for Masked Autoencoders

https://github.com/liuxinyv/sazs

공식 코드

Delving into Shape-aware Zero-shot Semantic Segmentation

https://github.com/liuxy1103/GIDBIS

공식 코드

Graph Relation Distillation for Efficient Biomedical Instance Segmentation

https://github.com/liuxy1103/grdbis

공식 코드

Graph Relation Distillation for Efficient Biomedical Instance Segmentation

https://github.com/liuyuanwei98/ipmt

공식 코드

Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation

https://github.com/liuzhengzhe/3D-to-2D-Distillation-for-Indoor-Scene-Parsing

공식 코드

3D-to-2D Distillation for Indoor Scene Parsing

https://github.com/liuzhengzhe/One-Thing-One-Click

공식 코드

One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation

https://github.com/liuzhu-cv/paif

공식 코드

PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation

https://github.com/liuzhuang13/anytime

공식 코드

Anytime Dense Prediction with Confidence Adaptivity

https://github.com/liuziyang123/faminet

공식 코드

FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flow

https://github.com/liweiyangv/dpcl

공식 코드

Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning

https://github.com/liwenchao0615/zerrinnet

공식 코드

Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition

https://github.com/liwentomng/point2mask

공식 코드

Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport

https://github.com/lixiaotong97/dsu

공식 코드

Uncertainty Modeling for Out-of-Distribution Generalization

https://github.com/lixiaotong97/mc-beit

공식 코드

mc-BEiT: Multi-choice Discretization for Image BERT Pre-training

https://github.com/lixincn2015/Partial-Order-Pruning

공식 코드

Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search

https://github.com/liyaotang/contrastboundary

공식 코드

Contrastive Boundary Learning for Point Cloud Segmentation

https://github.com/liyuantsao/HoliSDiP

공식 코드

HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior

https://github.com/liyunsheng13/BDL

공식 코드

Bidirectional Learning for Domain Adaptation of Semantic Segmentation

https://github.com/lizhaoliu-Lec/CPCM

공식 코드

CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation

https://github.com/lizhaoliu-lec/ccse

공식 코드

Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks

https://github.com/lizhh268/FSSUWNet

공식 코드

FSSUWNet: Mitigating the Fragility of Pre-trained Models with Feature Enhancement for Few-Shot Semantic Segmentation in Underwater Images

https://github.com/lizhou-cs/mglmm

공식 코드

Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model

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