Cross-Domain Few-Shot

Benchmarks archive 2025-07-28

9 leaderboard tables shown for this task, 9 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

6 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 80 papers with code (141 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Few-Shot Learning with Graph Neural Networks
  10 Nov 2017 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.
• NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
  14 Apr 2025 4 repositories listed
  Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains.
• Cross-domain Few-shot Learning with Task-specific Adapters
  1 Jul 2021 4 repositories listed
  In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples.
• Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
  5 Feb 2024 2 repositories listed Syntology ran 9 of 11 samples · 2 unverified
  This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples.
• StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
  18 Feb 2023 2 repositories listed
  Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL.
• Universal Representations: A Unified Look at Multiple Task and Domain Learning
  6 Apr 2022 2 repositories listed
  We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network.
• Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
  1 Feb 2022 2 repositories listed Syntology ran 5 of 7 samples · 2 unverified
  This data enables self-supervised pre-training on the target domain, in addition to supervised pre-training on the source domain.
• Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
  13 Jan 2022 2 repositories listed
  The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on…
• On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers
  19 Oct 2021 2 repositories listed
  The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on…
• Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
  8 Jan 2021 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Current state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple, e.
• Cross-Domain Few-Shot Learning by Representation Fusion
  13 Oct 2020 2 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.
• A Broader Study of Cross-Domain Few-Shot Learning
  16 Dec 2019 2 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.
• Self-Supervised Learning For Few-Shot Image Classification
  14 Nov 2019 2 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.
• CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion
  2 May 2025 1 repository listed
  Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances.
• Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection
  6 Apr 2025 1 repository listed
  Foundation models pretrained on extensive datasets, such as GroundingDINO and LAE-DINO, have performed remarkably in the cross-domain few-shot object detection (CD-FSOD) task.
• Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment
  23 Feb 2025 1 repository listed
  Advancements in cross-modal feature extraction and integration have significantly enhanced performance in few-shot learning tasks.
• SAM-Aware Graph Prompt Reasoning Network for Cross-Domain Few-Shot Segmentation
  31 Dec 2024 1 repository listed
  In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability.
• Enhancing Masked Time-Series Modeling via Dropping Patches
  19 Dec 2024 1 repository listed
  This paper explores how to enhance existing masked time-series modeling by randomly dropping sub-sequence level patches of time series.
• FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation
  12 Dec 2024 1 repository listed
  Existing few-shot medical image segmentation (FSMIS) models fail to address a practical issue in medical imaging: the domain shift caused by different imaging techniques, which limits the applicability to current FSMIS…
• TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
  28 Nov 2024 1 repository listed
  To effectively and efficiently explore the potential of pre-trained models in transferring to target domain, our TAMT proposes a Hierarchical Temporal Tuning Network (HTTN), whose core involves local temporal-aware…
• Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning
  15 Nov 2024 1 repository listed
  This paper addresses the source-free CDFSL (SF-CDFSL) problem, tackling few-shot learning (FSL) in the target domain using only pre-trained models and a few target samples without source data or strategies.
• Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation
  29 Oct 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Cross-domain few-shot segmentation (CD-FSS) is proposed to first pre-train the model on a large-scale source-domain dataset, and then transfer the model to data-scarce target-domain datasets for pixel-level segmentation.
• Mind the Gap Between Prototypes and Images in Cross-domain Finetuning
  16 Oct 2024 1 repository listed
  However, in this paper, we find that there naturally exists a gap, which resembles the modality gap, between the prototype and image instance embeddings extracted from the frozen pre-trained backbone, and simply…
• RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation
  1 Oct 2024 1 repository listed
  Specifically, we formulate the EMD transportation process between the foreground support-query features, the texture structure aware weights generation method, which proposes to perform the sobel based image gradient…
• Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms
  12 Sep 2024 1 repository listed
  We conducted comprehensive evaluations on the MiniImageNet and FC100 datasets, specifically in the 5-way 1-shot and 5-way 5-shot scenarios.
• Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical Images
  1 Jul 2024 1 repository listed
  In this work, we facilitate the study of the performance of PEFT when adapting foundation models to medical image classification tasks.
• Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting
  24 Jun 2024 1 repository listed Syntology ran 5 of 9 samples · 4 unverified · 9 pointer-only (licence)
  However, most existing methods pay more attention to learning domain-adaptive inductive bias (meta-knowledge) through feature-wise manipulation or task diversity improvement while neglecting the phenomenon that deep…
• Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning
  21 Jun 2024 1 repository listed
  Inspired by the success of vanilla adversarial learning in Cross-Domain Few-Shot Learning, we take a step further and devise a model-agnostic and Primitive-Based Adversarial training (PBadv) method to deal with this…
• MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence
  29 May 2024 1 repository listed
  In cross-domain few-shot classification, \emph{nearest centroid classifier} (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities…
• Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation
  24 May 2024 1 repository listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
  Instead of completely relying on support images, we propose Self-Matching Transformation (SMT) to construct query-specific transformation matrices based on query images themselves to transform domain-specific query…

Syntology lines on 10 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.