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Cross-Domain Few-Shot

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코드

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

https://github.com/lovelyqian/ME-D2N_for_CDFSL

공식 코드

ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning

https://github.com/lovelyqian/Meta-FDMixup

공식 코드

Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data

https://github.com/lovelyqian/ntire2025_cdfsod

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NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/lovelyqian/styleadv-cdfsl

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StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning

https://github.com/lovelyqian/wave-SAN-CDFSL

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Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning

https://github.com/luhc666/asc-cdfsl

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Adaptive Semantic Consistency for Cross-domain Few-shot Classification

https://github.com/matt-su/dr-adapter

공식 코드

Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation

https://github.com/ml-jku/chef

공식 코드

Cross-Domain Few-Shot Learning by Representation Fusion

https://github.com/naeem-paeedeh/adapter

공식 코드

Cross-Domain Few-Shot Learning via Adaptive Transformer Networks

https://github.com/niejiahao1998/ifa

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Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining

https://github.com/ondrejbohdal/evograd

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EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

https://github.com/open-debin/bayesian_mqda

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Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

https://github.com/plai-group/simple-cnaps

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Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

https://github.com/primebo1/FAMNet

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FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation

https://github.com/pumpkinder/glip-cdfsod

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NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/punyajoy/rgfs_ecai

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Rationale-Guided Few-Shot Classification to Detect Abusive Language

https://github.com/qityy/droppatch

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Enhancing Masked Time-Series Modeling via Dropping Patches

https://github.com/raflaf/few-and-fewer

공식 코드

Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes

https://github.com/rashindrie/dipa

공식 코드

Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning

https://github.com/remimz/metacoco-iclr24

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MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation

https://github.com/sarinda251/cdfsl-v

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CDFSL-V: Cross-Domain Few-Shot Learning for Videos

https://github.com/shlee625/cxgrad

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Contextual Gradient Scaling for Few-Shot Learning

https://github.com/sungnyun/understanding-cdfsl

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Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty

https://github.com/taveraantonio/pixda

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Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation

https://github.com/tinkez/fap_cdfsc

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Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting

https://github.com/tmlr-group/CoPA

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Mind the Gap Between Prototypes and Images in Cross-domain Finetuning

https://github.com/tmlr-group/mokd

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MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

https://github.com/tomte812/chef

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Cross-Domain Few-Shot Learning by Representation Fusion

https://github.com/tricktreat/promptner

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PromptNER: Prompt Locating and Typing for Named Entity Recognition

https://github.com/vgsatorras/few-shot-gnn

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Few-Shot Learning with Graph Neural Networks

https://github.com/vico-uoe/universalrepresentations

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Universal Representations: A Unified Look at Multiple Task and Domain Learning

https://github.com/vision-kek/abcdfss

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Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

https://github.com/xuhuali-mxj/im-dcl

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Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning

https://github.com/xuhuali-mxj/stepspt

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Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning

https://github.com/ycm094/mg-fte-main

공식 코드

Mutually Guided Few-shot Learning for Relational Triple Extraction

https://github.com/youthhoo/afa_for_few_shot_learning

공식 코드

Adversarial Feature Augmentation for Cross-domain Few-shot Classification

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