Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification | Paper Citation Record

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This paper cites In- trinsic dimensionality explains the effectiveness of language model fine-tuning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification In- trinsic dimensionality explains the effectiveness of language model fine-tuning

Reference 1

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This paper cites Flamingo: a visual language model for few-shot learning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Flamingo: a visual language model for few-shot learning

Reference 2

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

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Food-101–mining discriminative components with random forests

Reference 3

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This paper cites Convex Optimiza- tion.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Convex Optimiza- tion

Reference 4

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This paper cites Olora: Orthonormal low-rank adapta- tion of large language models, 2024.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Olora: Orthonormal low-rank adapta- tion of large language models, 2024

Reference 5

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This paper cites Domain prompt learning with quaternion networks.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Domain prompt learning with quaternion networks

Reference 6

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This paper cites Plot: Prompt learning with optimal transport for vision-language models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Plot: Prompt learning with optimal transport for vision-language models

Reference 7

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This paper cites Unleashing the power of meta-tuning for few-shot generalization through sparse interpolated experts.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Unleashing the power of meta-tuning for few-shot generalization through sparse interpolated experts

Reference 8

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This paper cites Describing textures in the wild.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Describing textures in the wild

Reference 9

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This paper cites Har- monizing generalization and personalization in federated prompt learning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Har- monizing generalization and personalization in federated prompt learning

Reference 10

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This paper cites Imagenet: A large-scale hierarchical image database.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Imagenet: A large-scale hierarchical image database

Reference 11

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This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification QLoRA: Efficient Finetuning of Quantized LLMs

Reference 12

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This paper cites A baseline for few-shot image clas- sification.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification A baseline for few-shot image clas- sification

Reference 13

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This paper cites Domain-agnostic mutual prompting for unsuper- vised domain adaptation.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Domain-agnostic mutual prompting for unsuper- vised domain adaptation

Reference 14

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This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 15

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This paper cites Instance-based max-margin for practical few-shot recognition.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Instance-based max-margin for practical few-shot recognition

Reference 16

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This paper cites ROSA: Random orthogonal subspace adaptation.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification ROSA: Random orthogonal subspace adaptation

Reference 17

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This paper cites Clip-adapter: Better vision-language models with feature adapters.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Clip-adapter: Better vision-language models with feature adapters

Reference 18

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This paper cites Parameter-efficient fine-tuning with discrete fourier transform.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Parameter-efficient fine-tuning with discrete fourier transform

Reference 19

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Unresolved cited work

Reference 20

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This paper cites Exploring the frontier of vision- language models: A survey of current methodologies and future directions, 2024.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Exploring the frontier of vision- language models: A survey of current methodologies and future directions, 2024

Reference 21

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This paper cites Anchor- based robust finetuning of vision-language models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Anchor- based robust finetuning of vision-language models

Reference 22

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This paper cites LoRA+: Effi- cient low rank adaptation of large models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification LoRA+: Effi- cient low rank adaptation of large models

Reference 23

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This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 24

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This paper cites LoRA: Low-rank adaptation of large language models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification LoRA: Low-rank adaptation of large language models

Reference 25

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This paper cites van Rijn.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification van Rijn

Reference 26

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 27

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This paper cites Singular Value Decomposition , pages 1174–1177.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Singular Value Decomposition , pages 1174–1177

Reference 28

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This paper cites Combining weakly and webly supervised learning for classifying food images, 2017.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Combining weakly and webly supervised learning for classifying food images, 2017

Reference 29

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This paper cites Maple: Multi-modal prompt learning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Maple: Multi-modal prompt learning

Reference 30

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This paper cites Self-regulating prompts: Foundational model adaptation without forgetting.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Self-regulating prompts: Foundational model adaptation without forgetting

Reference 31

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This paper cites Memory-efficient fine-tuning of compressed large language models via sub-4-bit integer quantization, 2023.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Memory-efficient fine-tuning of compressed large language models via sub-4-bit integer quantization, 2023

Reference 32

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This paper cites NOLA: Compressing lora using linear combination of random ba- sis.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification NOLA: Compressing lora using linear combination of random ba- sis

Reference 33

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This paper cites VeRA: Vector-based random matrix adaptation.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification VeRA: Vector-based random matrix adaptation

Reference 34

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This paper cites 3d object representations for fine-grained categorization.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification 3d object representations for fine-grained categorization

Reference 35

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This paper cites Beyond max-margin: Class margin equi- librium for few-shot object detection.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Beyond max-margin: Class margin equi- librium for few-shot object detection

Reference 36

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This paper cites Measuring the intrinsic dimension of objective landscapes.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Measuring the intrinsic dimension of objective landscapes

Reference 37

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This paper cites Inflora: Interference-free low-rank adaptation for continual learning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Inflora: Interference-free low-rank adaptation for continual learning

Reference 38

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This paper cites Svft: Parameter-efficient fine-tuning with singular vectors,.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Svft: Parameter-efficient fine-tuning with singular vectors,

Reference 39

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This paper cites Fine-Grained Visual Classification of Aircraft.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Fine-Grained Visual Classification of Aircraft

Reference 40

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This paper cites Transductive zero-shot and few-shot clip.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Transductive zero-shot and few-shot clip

Reference 41

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This paper cites PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 42

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This paper cites Automated flower classification over a large number of classes.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Automated flower classification over a large number of classes

Reference 43

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This paper cites Black box few-shot adaptation for vision- language models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Black box few-shot adaptation for vision- language models

Reference 44

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Unresolved cited work

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Cats and dogs

Reference 46

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Unresolved cited work

Reference 47

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This paper cites Semi- orthogonal low-rank matrix factorization for deep neural net- works.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Semi- orthogonal low-rank matrix factorization for deep neural net- works

Reference 48

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This paper cites Learning transferable visual models from natural language supervision, 2021.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Learning transferable visual models from natural language supervision, 2021

Reference 49

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Observation b9cff031-89b6-4e38-8a7a-fdb4c491f279 · outbound

This paper cites Low-rank matrix factorization for deep neural network training with high- dimensional output targets.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Low-rank matrix factorization for deep neural network training with high- dimensional output targets

Reference 50

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This paper cites Model-agnostic graph regularization for few-shot learning, 2021.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Model-agnostic graph regularization for few-shot learning, 2021

Reference 51

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Observation 575c10ae-f87e-4643-9e58-64d8ec42dd52 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 52

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Block- mix: Meta regularization and self-calibrated inference for metric-based meta-learning

Reference 53

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This paper cites Argue: Attribute-guided prompt tuning for vision-language models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Argue: Attribute-guided prompt tuning for vision-language models

Reference 54

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This paper cites Attention is all you need.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Attention is all you need

Reference 55

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Lora-ga: Low-rank adaptation with gradient approximation, 2024

Reference 56

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This paper cites Orthogonal Subspace Learning for Language Model Continual Learning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Orthogonal Subspace Learning for Language Model Continual Learning

Reference 57

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 58

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This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Sun database: Large-scale scene recognition from abbey to zoo

Reference 59

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Reference 60

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Mma: Multi-modal adapter for vision-language models

Reference 61

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This paper cites Visual- language prompt tuning with knowledge-guided context op- timization.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Visual- language prompt tuning with knowledge-guided context op- timization

Reference 62

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Tcp:textual- based class-aware prompt tuning for visual-language model

Reference 63

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This paper cites Lever- aging cross-modal neighbor representation for improved clip classification.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Lever- aging cross-modal neighbor representation for improved clip classification

Reference 64

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Task residual for tuning vision-language models

Reference 65

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Low-rank few-shot adaptation of vision-language models

Reference 66

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This paper cites Sim- ple semantic-aided few-shot learning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Sim- ple semantic-aided few-shot learning

Reference 67

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This paper cites Dept: Decoupled prompt tuning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Dept: Decoupled prompt tuning

Reference 68

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Adaptive budget allocation for parameter-efficient fine- tuning

Reference 69

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This paper cites Tip- adapter: Training-free adaption of clip for few-shot classifi- cation.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Tip- adapter: Training-free adaption of clip for few-shot classifi- cation

Reference 70

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This paper cites Ex- tracting deep neural network bottleneck features using low- rank matrix factorization.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Ex- tracting deep neural network bottleneck features using low- rank matrix factorization

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:45:16.921406Z

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This paper cites Large language models are good prompt learners for low-shot image classification.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Large language models are good prompt learners for low-shot image classification

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:45:16.910812Z

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This paper cites Conditional prompt learning for vision-language models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Conditional prompt learning for vision-language models

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:45:16.899434Z

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This paper cites Learning to prompt for vision-language models.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Learning to prompt for vision-language models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:45:16.888345Z

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Observation 6932b712-6041-4660-8d9a-e2112eb4d5d9 · outbound

This paper cites Prompt-aligned gradient for prompt tuning.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Prompt-aligned gradient for prompt tuning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:45:16.875906Z

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This paper cites Not all features mat- ter: Enhancing few-shot clip with adaptive prior refinement.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Not all features mat- ter: Enhancing few-shot clip with adaptive prior refinement

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:45:16.863408Z

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Observation ed9de738-88b5-49ff-abf3-0c7108e281b6 · outbound

This paper cites Laplacian regularized few-shot learning, 2021.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Laplacian regularized few-shot learning, 2021

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:45:16.852633Z

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Observation ae60a9b6-f617-4f0c-ac04-a03fbad12282 · outbound

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Unresolved cited work

Reference 78

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Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification Unresolved cited work

Reference 79

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Observation 8c0d9668-ec0a-4747-afc1-4a8c8bf335ff · outbound

This paper cites 7 to 16, the univariate experiment on other experiments presents similar trend to that of ImageNet in Fig.

Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification 7 to 16, the univariate experiment on other experiments presents similar trend to that of ImageNet in Fig

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:45:16.820341Z

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