Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains | Paper Citation Record

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This paper cites Cross-Domain Few-Shot Learning by Representation Fusion.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Cross-Domain Few-Shot Learning by Representation Fusion

Reference 1

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Layer Normalization

Reference 2

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Meta-learning with adaptive hyperparameters

Reference 3

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This paper cites Strong baselines for parameter-efficient few-shot fine-tuning.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Strong baselines for parameter-efficient few-shot fine-tuning

Reference 4

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This paper cites Food-101 – mining discriminative components with random forests.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Food-101 – mining discriminative components with random forests

Reference 5

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This paper cites Memory efficient meta-learning with large images.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Memory efficient meta-learning with large images

Reference 6

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This paper cites Emerging properties in self-supervised vision transformers.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Emerging properties in self-supervised vision transformers

Reference 7

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This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 8

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This paper cites A closer look at few-shot classification.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains A closer look at few-shot classification

Reference 9

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This paper cites Meta-baseline: Exploring simple meta-learning for few-shot learning.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Meta-baseline: Exploring simple meta-learning for few-shot learning

Reference 10

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Describing textures in the wild

Reference 11

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This paper cites RandAugment: Practical automated data augmentation with a reduced search space.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains RandAugment: Practical automated data augmentation with a reduced search space

Reference 12

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Unresolved cited work

Reference 13

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This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 14

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This paper cites A baseline for few-shot image classification.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains A baseline for few-shot image classification

Reference 15

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This paper cites Dietterich, Richard H.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Dietterich, Richard H

Reference 16

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This paper cites Crosstransformers: spatially-aware few-shot transfer.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Crosstransformers: spatially-aware few-shot transfer

Reference 17

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains An image is worth 16x16 words: Transformers for image recognition at scale

Reference 18

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This paper cites Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark

Reference 19

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This paper cites A unified few-shot classification benchmark to compare transfer and meta learning approaches.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains A unified few-shot classification benchmark to compare transfer and meta learning approaches

Reference 20

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This paper cites Generalized meta-fdmixup: Cross-domain few-shot learning guided by labeled target data.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Generalized meta-fdmixup: Cross-domain few-shot learning guided by labeled target data

Reference 21

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This paper cites Codella, Leonid Karlinsky, James V.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Codella, Leonid Karlinsky, James V

Reference 22

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This paper cites A brief survey on semantic segmentation with deep learning.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains A brief survey on semantic segmentation with deep learning

Reference 23

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Spatial pyramid pooling in deep convolutional networks for visual recognition

Reference 24

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Deep residual learning for image recognition

Reference 25

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Few-shot learning via repurposing ensemble of black-box models

Reference 26

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Cross attention network for few-shot classification

Reference 27

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Squeeze-and-excitation networks

Reference 28

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Hospedales

Reference 29

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Densely connected convolutional networks

Reference 30

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Unresolved cited work

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains The Quick, Draw! – A.I

Reference 32

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Learning multiple layers of features from tiny images

Reference 33

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Human-level concept learning through probabilistic program induction

Reference 34

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Lecun, L

Reference 35

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Kosiorek, Seungjin Choi, and Yee Whye Teh

Reference 36

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 37

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Universal representation learning from multiple domains for few-shot classification

Reference 38

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Cross-domain few-shot learning with task-specific adapters

Reference 39

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Maire, Serge J

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Swin transformer: Hierarchical vision transformer using shifted windows

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains A closer look at few-shot classification again

Reference 42

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Unresolved cited work

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Distance-based image classification: Generalizing to new classes at near-zero cost

Reference 44

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Automated flower classification over a large number of classes

Reference 45

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Pytorch: An imperative style, high-performance deep learning library

Reference 46

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Contextual squeeze-and-excitation for efficient few-shot image classification

Reference 47

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Discriminative sample-guided and parameter-efficient feature space adaptation for cross-domain few-shot learning

Reference 48

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Multi- instance attention network for few-shot learning

Reference 49

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Learning transferable visual models from natural language supervision

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Designing network design spaces

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Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Fast and flexible multi-task classification using conditional neural adaptive processes

Reference 52

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This paper cites Berg, and Li Fei-Fei.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Berg, and Li Fei-Fei

Reference 53

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Observation 14c84a2e-ba96-413a-9b8c-55ea25e9db11 · outbound

This paper cites Optimized Generic Feature Learning for Few-shot Classification across Domains.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Optimized Generic Feature Learning for Few-shot Classification across Domains

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:22:29.990040Z

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Observation c36a99bd-ae68-4b5f-b4b3-5b43c5a98188 · outbound

This paper cites FGVCx fungi classification challenge 2018.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains FGVCx fungi classification challenge 2018

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.246123Z

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Observation 72daed85-0cd5-437a-a0bc-f83d70a21618 · outbound

This paper cites FiT: Parameter efficient few-shot transfer learning for personalized and federated image classification.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains FiT: Parameter efficient few-shot transfer learning for personalized and federated image classification

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.236927Z

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Observation 4d3b34be-f38c-4a67-8418-eadf948eaed5 · outbound

This paper cites Prototypical networks for few-shot learning.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Prototypical networks for few-shot learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.228813Z

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Observation 09f345a9-5fee-4151-8b37-80c54851c532 · outbound

This paper cites The German Traffic Sign Recognition Benchmark: A multi-class classification competition.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains The German Traffic Sign Recognition Benchmark: A multi-class classification competition

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.220333Z

Source-reported events for the cited work

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Observation 87d6d282-7a31-44c8-a486-9997324bc691 · outbound

This paper cites Rethinking few-shot image classification: a good embedding is all you need? In Computer Vision–ECCV 2020, 2020.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Rethinking few-shot image classification: a good embedding is all you need? In Computer Vision–ECCV 2020, 2020

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.211003Z

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Observation fed35e5c-c73c-40c0-a70d-88e8c23d1657 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Training data-efficient image transformers & distillation through attention

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.134322Z

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Observation 447f93d2-8144-4cb5-9dee-91101bad1073 · outbound

This paper cites Meta-dataset: A dataset of datasets for learning to learn from few examples.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Meta-dataset: A dataset of datasets for learning to learn from few examples

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.126324Z

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Observation b1ecc273-eaef-4662-ad87-6d1cbf06a237 · outbound

This paper cites Learning a universal template for few-shot dataset generalization.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Learning a universal template for few-shot dataset generalization

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.116687Z

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Observation 39713b35-c65f-47b5-bbe1-dc38ab64d0bc · outbound

This paper cites Cross-domain few-shot classification via learned feature-wise transformation.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Cross-domain few-shot classification via learned feature-wise transformation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.109499Z

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Observation fac6d03e-5b9c-4f56-9fe8-00bddbee9790 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.101191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3d8e5822-07e2-4334-83d7-d7b2c1dfe7ae · outbound

This paper cites Matching networks for one shot learning.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Matching networks for one shot learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.093586Z

Source-reported events for the cited work

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Observation 24521050-a620-4d08-aa98-65d900924ca4 · outbound

This paper cites an unresolved cited work.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:22:30.085382Z

Source-reported events for the cited work

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Observation 339710fb-f76a-4ab3-8e00-57e3b1c1214b · outbound

This paper cites Feature extractor stacking for cross-domain few-shot learning.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Feature extractor stacking for cross-domain few-shot learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.077307Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4825e7b7-a047-43cb-bba4-19dcdfea6496 · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Generalizing from a few examples: A survey on few-shot learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.068697Z

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Observation 15248963-2678-4396-af30-b793878c3175 · outbound

This paper cites Simmim: A simple framework for masked image modeling.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Simmim: A simple framework for masked image modeling

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.060488Z

Source-reported events for the cited work

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Observation 90e639c8-c6bf-43e3-bd55-73df06930ee5 · outbound

This paper cites Exploring efficient few-shot adaptation for vision transformers.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Exploring efficient few-shot adaptation for vision transformers

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.052668Z

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Observation fdd19aad-6747-419d-a8d3-bf1fb69f85f4 · outbound

This paper cites Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.043998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 71620446-a41b-4aa8-be8a-ed5ed1935681 · outbound

This paper cites Tip-adapter: Training-free adaption of CLIP for few-shot classification.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Tip-adapter: Training-free adaption of CLIP for few-shot classification

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:30.034587Z

Source-reported events for the cited work

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Observation 4b4a2154-c892-4e80-afc1-ecbe8125ff2e · outbound

This paper cites an unresolved cited work.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:22:30.025585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b8916bce-b3b9-4c05-8f39-f37a0a426616 · outbound

This paper cites an unresolved cited work.

Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains Unresolved cited work

Reference 2024

Resolution: verified exact; raw_fallback, observed 2026-08-06T21:22:29.978544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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