Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation | Paper Citation Record

Observation 717aea04-fbb7-4b36-a47f-eae4399a6ff9 · outbound

This paper cites Novel dataset for ﬁne-grained image cate- gorization.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Novel dataset for ﬁne-grained image cate- gorization

Reference 1

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Observation f664c052-df3c-4cf1-8d42-b217e5ddd7b0 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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Observation 1caa6c55-4ddf-4a8d-adcc-84b1f27c9f74 · outbound

This paper cites Improving vision transformers by revis- iting high-frequency components.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Improving vision transformers by revis- iting high-frequency components

Reference 3

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This paper cites Visual prompting via image inpaint- ing.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Visual prompting via image inpaint- ing

Reference 4

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This paper cites Tinytl: Reduce memory, not parameters for efﬁcient on-device learn - ing.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Tinytl: Reduce memory, not parameters for efﬁcient on-device learn - ing

Reference 5

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This paper cites Visual prompting for adversarial robustness.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Visual prompting for adversarial robustness

Reference 6

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Observation e487f2c9-8d1b-4464-b4e5-549c3e96d032 · outbound

This paper cites Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

Reference 7

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Observation 35423faf-28b8-47f3-a982-4b43fb5b5d95 · outbound

This paper cites AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Reference 8

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Observation 9cdd2c09-fe9f-4826-86cf-a53dc937f6c9 · outbound

This paper cites An Empirical Study of Training Self-Supervised Vision Transformers.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation An Empirical Study of Training Self-Supervised Vision Transformers

Reference 9

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Observation a4df3cae-56ac-4efc-887f-f2085a7155dd · outbound

This paper cites Vision Transformer Adapter for Dense Predictions.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Vision Transformer Adapter for Dense Predictions

Reference 10

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This paper cites Fast fourier convolu - tion.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Fast fourier convolu - tion

Reference 11

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Observation ee33c21a-e3e0-40c4-831e-a14791578879 · outbound

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

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Imagenet: A large-scale hierarchical image database

Reference 12

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Observation 64feb735-c0e4-41ae-944c-0c36b1a42a84 · outbound

This paper cites Circnn: accelerating and compressing deep neural net - works using block-circulant weight matrices.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Circnn: accelerating and compressing deep neural net - works using block-circulant weight matrices

Reference 13

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Observation 7e23d932-ca5c-4065-880f-016c8806a613 · outbound

This paper cites LPT: Long-tailed Prompt Tuning for Image Classification.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation LPT: Long-tailed Prompt Tuning for Image Classification

Reference 14

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This paper cites Attention is not all you need: Pure attention loses rank dou- bly exponentially with depth.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Attention is not all you need: Pure attention loses rank dou- bly exponentially with depth

Reference 15

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Observation 01fa4f5f-c303-4220-8d89-a163c6e730b5 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 16

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Observation 472ad5ca-3ab7-41e9-b2bc-bd5577cd5e95 · outbound

This paper cites Parameter-Efficient Fine-Tuning with Discrete Fourier Transform.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Parameter-Efficient Fine-Tuning with Discrete Fourier Transform

Reference 17

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Observation 9c93182c-7f74-4050-aae4-0c31088ef66b · outbound

This paper cites Fine-grained car detection for vi- sual census estimation.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Fine-grained car detection for vi- sual census estimation

Reference 18

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Observation 1873755e-3531-43de-a10b-bec35f34867b · outbound

This paper cites Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

Reference 19

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Observation 4ab9fdd0-04a1-4763-9f9f-fb0b0f922cd9 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Masked autoencoders are scalable vision learners

Reference 20

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Observation 7b56469f-19d4-4247-804e-b44385ad9514 · outbound

This paper cites Parameter-efﬁcient transfe r learning for nlp.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Parameter-efﬁcient transfe r learning for nlp

Reference 21

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

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

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This paper cites Vi- sual prompt tuning.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Vi- sual prompt tuning

Reference 23

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Observation c9f9f92a-5067-4445-9063-aea4dd969a1a · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation VeRA: Vector-based Random Matrix Adaptation

Reference 24

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This paper cites Single-image depth estimation based on fourier do- main analysis.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Single-image depth estimation based on fourier do- main analysis

Reference 25

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Observation 814d3d92-66b2-4a00-8036-d333ab600bc0 · outbound

This paper cites FNet: Mixing Tokens with Fourier Transforms.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation FNet: Mixing Tokens with Fourier Transforms

Reference 26

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Observation 349dc856-fa93-478b-a184-0fd2c2154279 · outbound

This paper cites Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning

Reference 27

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Observation 110a6be5-9584-4e65-b4ae-08da1601cbe6 · outbound

This paper cites Hierarchical Side-Tuning for Vision Transformers.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Hierarchical Side-Tuning for Vision Transformers

Reference 28

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Observation b6c5f422-c804-4123-bfb2-4c0a1e35cce6 · outbound

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Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization

Reference 29

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This paper cites Peft: State-of-the-art parameter-efﬁcient ﬁne-tuning me th- ods.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Peft: State-of-the-art parameter-efﬁcient ﬁne-tuning me th- ods

Reference 30

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

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Automated ﬂower classiﬁcation over a large number of classes

Reference 31

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Observation f48bb99c-1587-4458-8070-ccf9bf6eabf2 · outbound

This paper cites How Do Vision Transformers Work?.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation How Do Vision Transformers Work?

Reference 32

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This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 33

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Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation AdapterHub: A Framework for Adapting Transformers

Reference 34

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This paper cites Learn- ing transferable visual models from natural language super - vision.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Learn- ing transferable visual models from natural language super - vision

Reference 35

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This paper cites Global ﬁlter networks for image classiﬁcation.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Global ﬁlter networks for image classiﬁcation

Reference 36

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Observation 4ad5cf8a-cda1-408f-b38c-d881dad8d8ce · outbound

This paper cites Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers

Reference 37

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Observation 2961c6fb-e505-47ef-8bc9-dd11c0febaec · outbound

This paper cites Multitask Vision-Language Prompt Tuning.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Multitask Vision-Language Prompt Tuning

Reference 38

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Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Inception transformer

Reference 39

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Observation b3e4e4e2-4d76-4aa1-a005-7e046aedc336 · outbound

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Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation FFT-based Dynamic Token Mixer for Vision

Reference 40

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Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Mlp- mixer: An all-mlp architecture for vision

Reference 41

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This paper cites Building a bird recognition app and large scale dataset with citizen scientists: The ﬁne print in ﬁne-grain ed dataset collection.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Building a bird recognition app and large scale dataset with citizen scientists: The ﬁne print in ﬁne-grain ed dataset collection

Reference 42

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Observation 1cffeeed-c152-49e4-a654-0b0ced6c68cc · outbound

This paper cites Attention is all you need.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Attention is all you need

Reference 43

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This paper cites Pivot: Prompting for video con- tinual learning.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Pivot: Prompting for video con- tinual learning

Reference 44

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This paper cites The caltech-ucsd birds-200-2011 dataset.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation The caltech-ucsd birds-200-2011 dataset

Reference 45

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This paper cites Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice

Reference 46

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This paper cites P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting

Reference 47

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Observation dbb0f8b5-4bfe-486f-bd5f-5ad835508d38 · outbound

This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 48

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Observation a2d3f9d2-9dd5-40be-a06a-3188a1e45e25 · outbound

This paper cites Learning to prompt for continual learning.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Learning to prompt for continual learning

Reference 49

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Observation 8c517727-7360-4b90-b21f-badb03d20828 · outbound

This paper cites Generative visual prompt: Unifying distributional control of pre-trained generative models.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Generative visual prompt: Unifying distributional control of pre-trained generative models

Reference 50

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Observation 2d39b6d1-07f2-454a-9c12-d016bc8808ed · outbound

This paper cites Fda: Fourier domain adaptation for semantic segmentation.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Fda: Fourier domain adaptation for semantic segmentation

Reference 51

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Observation cfbb1a37-2b04-4736-b1c8-256fa2ec8ed4 · outbound

This paper cites Improving Visual Prompt Tuning for Self-supervised Vision Transformers.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Improving Visual Prompt Tuning for Self-supervised Vision Transformers

Reference 52

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Observation f1763e42-3d62-444c-ad0f-ba87d004bf99 · outbound

This paper cites Bit ﬁt: Simple parameter-efﬁcient ﬁne-tuning for transformer-ba sed masked language-models.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Bit ﬁt: Simple parameter-efﬁcient ﬁne-tuning for transformer-ba sed masked language-models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T10:24:01.345543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d85c084-400b-4158-8f00-52ad4f1cae7e · outbound

This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T10:24:01.349367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c116fe5-8305-4b58-8588-4e46af4710ae · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation mixup: Beyond Empirical Risk Minimization

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T10:24:01.353694Z

Source-reported events for the cited work

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Observation de3158b2-254a-4698-9400-25f2f538aae5 · outbound

This paper cites Point- clip: Point cloud understanding by clip.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Point- clip: Point cloud understanding by clip

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:24:02.067310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cceef198-308f-4ffc-a8c2-da8d8a79f1bb · outbound

This paper cites Neural Prompt Search.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Neural Prompt Search

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T10:24:01.360437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5dc3a1c-f6cc-4fba-9ec6-58c09dbb3168 · outbound

This paper cites an unresolved cited work.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:24:02.053347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15108a77-5d41-4ad2-92bd-a32fd3e34939 · outbound

This paper cites an unresolved cited work.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:24:02.039752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f33ecfb7-a7f2-446a-8b35-195e4eeea792 · outbound

This paper cites an unresolved cited work.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:24:02.026461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 17c8fc96-7157-49bb-b490-82cb7b0fe815 · outbound

This paper cites an unresolved cited work.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:24:02.013645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a0318b7c-d92d-4610-b432-0234191fe701 · outbound

This paper cites an unresolved cited work.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:24:02.000650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d4f01145-f5ab-4981-be54-e6035d6e5c82 · outbound

This paper cites an unresolved cited work.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:24:01.988910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 284dcf08-df3d-49d2-884d-34d6ac9f9247 · outbound

This paper cites We re-introduce FreqFit and LoRA equations to facilitate the proofs below.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation We re-introduce FreqFit and LoRA equations to facilitate the proofs below

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:24:01.976459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0681dccc-25ac-4a83-9bff-5f98cb0794f4 · outbound

This paper cites an unresolved cited work.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:24:01.963479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5503ef03-64cd-4de5-9934-214d314e5b32 · outbound

This paper cites For each position (in frequency domains) in the H× W grid, K contains a unique ﬁlter for each of the D channels.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation For each position (in frequency domains) in the H× W grid, K contains a unique ﬁlter for each of the D channels

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:24:01.949062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dbc5b8da-5938-4429-9fea-8e17e201d5ee · outbound

This paper cites 12 is a token-speciﬁc modiﬁcation, where the D- dimensional representation of each token is updated.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation 12 is a token-speciﬁc modiﬁcation, where the D- dimensional representation of each token is updated

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:24:01.936114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aa3f181b-fefb-4dcb-aaa9-5a606cfbc9fe · outbound

This paper cites 12 is the same across all tokens, meaning it only introduces channel-wise dependencies.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation 12 is the same across all tokens, meaning it only introduces channel-wise dependencies

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:24:01.923425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d846fcc-1849-43ca-bf26-d837de635943 · outbound

This paper cites Following [ 23], we conduct a grid search to ﬁnd the tuning-speciﬁc hyper-parameters, learning rate, and weig ht decay values using val set of each task, as shown in Tab.

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation Following [ 23], we conduct a grid search to ﬁnd the tuning-speciﬁc hyper-parameters, learning rate, and weig ht decay values using val set of each task, as shown in Tab

Reference 69

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T10:24:01.911211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Reference resolution

Outbound references

Pith citing papers