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/miclab-unicamp/medical-imaging-tutorial

공식 코드

Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide

https://github.com/microndla/mobilevitv3

공식 코드

MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features

https://github.com/microsoft/CSWin-Transformer

공식 코드

CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows

https://github.com/microsoft/Focal-Transformer

공식 코드

Focal Self-attention for Local-Global Interactions in Vision Transformers

https://github.com/microsoft/FocalNet

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Focal Modulation Networks

https://github.com/microsoft/GLIP

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GLIPv2: Unifying Localization and Vision-Language Understanding

https://github.com/microsoft/ProDA

공식 코드

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation

https://github.com/microsoft/Relation-Aware-Global-Attention-Networks

공식 코드

Relation-Aware Global Attention for Person Re-identification

https://github.com/microsoft/SNR

공식 코드

Style Normalization and Restitution for Domain Generalization and Adaptation

https://github.com/microsoft/SPACH

공식 코드

When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism

https://github.com/microsoft/SoftTeacher

공식 코드

End-to-End Semi-Supervised Object Detection with Soft Teacher

https://github.com/microsoft/Swin-Transformer

공식 코드

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/microsoft/X-Decoder

공식 코드

Generalized Decoding for Pixel, Image, and Language

https://github.com/microsoft/activemlp

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Active Token Mixer

https://github.com/microsoft/cream

공식 코드

Searching the Search Space of Vision Transformer

https://github.com/microsoft/swin3d

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Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

https://github.com/microsoft/torchgeo

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SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

https://github.com/microsoft/transductive-vos.pytorch

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A Transductive Approach for Video Object Segmentation

https://github.com/microsoft/unilm/tree/master/beit

공식 코드

BEiT: BERT Pre-Training of Image Transformers

https://github.com/microsoft/unilm/tree/master/beit2

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BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

https://github.com/midas-research/persuasion-advertisements

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Persuasion Strategies in Advertisements

https://github.com/miemieyanga/ResiDualGAN-DRDG

공식 코드

ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation

https://github.com/miguelag99/efficient-instance-prediction

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Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction

https://github.com/mihirp1998/Slot-TTA

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Test-time Adaptation with Slot-Centric Models

https://github.com/mil-tokyo/MCD_DA

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Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

https://github.com/mileswyn/samihs

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SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation

https://github.com/mililab/roma

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RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing

https://github.com/minaghadimiatigh/hyperbolicimagesegmentation

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Hyperbolic Image Segmentation

https://github.com/mindflow-institue/annotation_free_fewshot

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Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach

https://github.com/mindflow-institue/daeformer

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DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation

https://github.com/mindflow-institue/ssct

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Self-supervised Semantic Segmentation: Consistency over Transformation

https://github.com/mindflow-institue/transception

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Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

https://github.com/mindflow-institue/waveformer

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Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers

https://github.com/mindspore-ai/models/tree/master/research/cv/TokenFusion

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Multimodal Token Fusion for Vision Transformers

https://github.com/ming1993li/self-supervised-geometric

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Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond

https://github.com/minghanli/boxvis

공식 코드

BoxVIS: Video Instance Segmentation with Box Annotations

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