Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs | Paper Citation Record

Observation 0398907c-d6d2-47b2-a837-b297d1ec5d04 · outbound

This paper cites Meta-adapters: Parameter ef- ficient few-shot fine-tuning through meta-learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta-adapters: Parameter ef- ficient few-shot fine-tuning through meta-learning

Reference 1

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This paper cites Meta-learning with differentiable closed-form solvers.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta-learning with differentiable closed-form solvers

Reference 2

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Observation 2773f58c-774f-4cf9-913a-1b49a686fb92 · outbound

This paper cites Language models are few-shot learners.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Language models are few-shot learners

Reference 3

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This paper cites Cross-Domain Few-Shot Learning with Meta Fine-Tuning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Cross-Domain Few-Shot Learning with Meta Fine-Tuning

Reference 4

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Observation 6164bfe4-553e-4a0e-954a-1ee7e3d32d9a · outbound

This paper cites LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs

Reference 5

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This paper cites Meta-learning via language model in-context tuning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta-learning via language model in-context tuning

Reference 6

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Observation 9c0105b8-6747-40e2-9375-8cb96af366c5 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Scaling Instruction-Finetuned Language Models

Reference 7

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This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 8

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Observation 55d85c64-64ef-4bd7-bef9-1f6995a75538 · outbound

This paper cites A Survey on In-context Learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs A Survey on In-context Learning

Reference 9

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Observation 4397f209-fc49-4de8-9e79-5a580d1374fa · outbound

This paper cites Contrastive Model Inversion for Data-Free Knowledge Distillation.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Contrastive Model Inversion for Data-Free Knowledge Distillation

Reference 10

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Observation 63bccb93-515f-471f-a58e-0cb33bd1cbfb · outbound

This paper cites Up to 100x faster data- free knowledge distillation.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Up to 100x faster data- free knowledge distillation

Reference 11

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Observation 5708a13d-bfd0-4324-b240-d6de891addbe · outbound

This paper cites Context-Aware Meta-Learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Context-Aware Meta-Learning

Reference 12

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This paper cites Model- agnostic meta-learning for fast adaptation of deep networks.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Model- agnostic meta-learning for fast adaptation of deep networks

Reference 13

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This paper cites Styleadv: Meta style adversarial training for cross-domain few-shot learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Styleadv: Meta style adversarial training for cross-domain few-shot learning

Reference 14

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This paper cites On the effectiveness of parameter-efficient fine-tuning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs On the effectiveness of parameter-efficient fine-tuning

Reference 15

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

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Clip-adapter: Better vision-language models with feature adapters

Reference 16

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This paper cites Know where you’re going: Meta-learning for parameter-efficient fine-tuning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Know where you’re going: Meta-learning for parameter-efficient fine-tuning

Reference 17

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This paper cites Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning

Reference 18

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Observation 7376e1aa-cfe5-42e4-9844-59777a4178c4 · outbound

This paper cites A broader study of cross-domain few-shot learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs A broader study of cross-domain few-shot learning

Reference 19

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This paper cites Gradvit: 9 Gradient inversion of vision transformers.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Gradvit: 9 Gradient inversion of vision transformers

Reference 20

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This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 21

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This paper cites Towards a unified view of parameter-efficient transfer learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Towards a unified view of parameter-efficient transfer learning

Reference 22

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This paper cites Revisiting data-free knowledge distilla- tion with poisoned teachers.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Revisiting data-free knowledge distilla- tion with poisoned teachers

Reference 23

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This paper cites Meta- learning the difference: preparing large language models for efficient adaptation.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta- learning the difference: preparing large language models for efficient adaptation

Reference 24

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This paper cites Parameter-efficient transfer learning for nlp.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Parameter-efficient transfer learning for nlp

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs LoRA: Low-Rank Adaptation of Large Language Models

Reference 26

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This paper cites Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference

Reference 27

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This paper cites Sparse model inversion: Ef- ficient inversion of vision transformers for data-free appli- cations.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Sparse model inversion: Ef- ficient inversion of vision transformers for data-free appli- cations

Reference 28

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Architecture, dataset and model- scale agnostic data-free meta-learning

Reference 29

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This paper cites Learning to Learn from APIs: Black-Box Data-Free Meta-Learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Learning to Learn from APIs: Black-Box Data-Free Meta-Learning

Reference 30

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Reference 31

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Diversity-aware meta visual prompting

Reference 32

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This paper cites OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

Reference 33

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Rethinking Efficient Tuning Methods from a Unified Perspective

Reference 34

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs All tokens matter: Token labeling for training better vision transform- ers

Reference 35

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Adaptive gradient-based meta-learning methods.Ad- vances in Neural Information Processing Systems, 32, 2019

Reference 36

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Token fusion: Bridging the gap between token pruning and token merging

Reference 37

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 38

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Surgical fine- tuning improves adaptation to distribution shifts

Reference 39

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Patch similarity aware data-free quantization for vision transformers

Reference 40

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Psaq-vit v2: Toward accurate and general data-free quanti- zation for vision transformers

Reference 41

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts

Reference 42

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Few- shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 43

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Small scale data-free knowledge distillation

Reference 44

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This paper cites Matcher: Segment anything with one shot using all-purpose feature matching.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Matcher: Segment anything with one shot using all-purpose feature matching

Reference 45

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs DFRD: Data-free robustness distillation 10 for heterogeneous federated learning

Reference 46

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This paper cites MetaICL: Learning to learn in context.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs MetaICL: Learning to learn in context

Reference 47

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This paper cites Meta learning to bridge vision and language models for mul- timodal few-shot learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta learning to bridge vision and language models for mul- timodal few-shot learning

Reference 48

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This paper cites On First-Order Meta-Learning Algorithms.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs On First-Order Meta-Learning Algorithms

Reference 49

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

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Automated flower classification over a large number of classes

Reference 50

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This paper cites Dynamicvit: Efficient vision transformers with dynamic token sparsification.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Dynamicvit: Efficient vision transformers with dynamic token sparsification

Reference 51

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This paper cites Data-free knowledge distillation for fine-grained visual cat- egorization.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Data-free knowledge distillation for fine-grained visual cat- egorization

Reference 52

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This paper cites Prototypical networks for few-shot learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Prototypical networks for few-shot learning

Reference 53

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This paper cites Meta-transfer learning for few-shot learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta-transfer learning for few-shot learning

Reference 54

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

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Training data-efficient image transformers & distillation through at- tention

Reference 55

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This paper cites Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation

Reference 56

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This paper cites Nayer: Noisy layer data generation for efficient and effective data-free knowl- edge distillation.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Nayer: Noisy layer data generation for efficient and effective data-free knowl- edge distillation

Reference 57

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

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta-dataset: A dataset of datasets for learning to learn from few examples

Reference 58

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This paper cites Matching networks for one shot learning.Ad- vances in neural information processing systems , 29, 2016.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Matching networks for one shot learning.Ad- vances in neural information processing systems , 29, 2016

Reference 59

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This paper cites Transformers learn in-context by gradient descent.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Transformers learn in-context by gradient descent

Reference 60

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This paper cites The Caltech-UCSD Birds-200- 2011 Dataset.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs The Caltech-UCSD Birds-200- 2011 Dataset

Reference 61

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This paper cites Generalizing to unseen domains: A survey on do- main generalization.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Generalizing to unseen domains: A survey on do- main generalization

Reference 62

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This paper cites De-confounded data-free knowledge distillation for handling distribution shifts.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs De-confounded data-free knowledge distillation for handling distribution shifts

Reference 63

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This paper cites Meta learning on a sequence of imbalanced domains with difficulty awareness.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta learning on a sequence of imbalanced domains with difficulty awareness

Reference 64

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta-learning without data via wasserstein distributionally- robust model fusion

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:49:03.269704Z

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This paper cites Task groupings regular- ization: Data-free meta-learning with heterogeneous pre- trained models.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Task groupings regular- ization: Data-free meta-learning with heterogeneous pre- trained models

Reference 66

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This paper cites Free: Faster and better data-free meta-learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Free: Faster and better data-free meta-learning

Reference 67

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This paper cites pi-tuning: Transferring multimodal foundation models with optimal multi-task inter- polation.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs pi-tuning: Transferring multimodal foundation models with optimal multi-task inter- polation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:49:03.214334Z

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This paper cites Mole: Mixture of lora experts.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Mole: Mixture of lora experts

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:49:03.197254Z

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This paper cites Meta-personalizing vision- language models to find named instances in video.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Meta-personalizing vision- language models to find named instances in video

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:49:03.176649Z

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Observation 8fee081b-75ed-4feb-9cc7-e8e1ccf8f167 · outbound

This paper cites 11 Dreaming to distill: Data-free knowledge transfer via deep- inversion.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs 11 Dreaming to distill: Data-free knowledge transfer via deep- inversion

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:49:03.157971Z

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This paper cites Bayesian model-agnostic meta-learning.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Bayesian model-agnostic meta-learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:49:03.140780Z

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This paper cites Data-free knowledge distillation via feature exchange and activation region constraint.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Data-free knowledge distillation via feature exchange and activation region constraint

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:49:03.119951Z

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Observation 8e0c5053-03fe-4f80-be6e-5286445c7357 · outbound

This paper cites recycle in-domain LoRAs.

Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs recycle in-domain LoRAs

Reference 74

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Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs Unresolved cited work

Reference 75

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