DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers | Paper Citation Record

Observation 677721b7-0231-4b2d-a17d-2abc1e478198 · outbound

This paper cites DeepMind Lab.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers DeepMind Lab

Reference 1

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Observation aba3a180-d93f-47a1-9ec9-a81c3d7599b9 · outbound

This paper cites Language models are few-shot learners.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Language models are few-shot learners

Reference 2

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Observation 5b16eae0-3f85-4de5-b389-b9d959349518 · outbound

This paper cites One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

Reference 3

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Observation 1f7c3ce4-9231-4410-bda4-dfb1316dc0f7 · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 4

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Observation 9101cb3d-2e76-4184-813e-e2dd98ed3b5e · outbound

This paper cites An empiri- cal study of training self-supervised vision transformers.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers An empiri- cal study of training self-supervised vision transformers

Reference 5

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Observation 55393677-eef1-44cf-91f5-77fef663ab60 · outbound

This paper cites Person re-identification by multi-channel parts-based cnn with improved triplet loss function.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Person re-identification by multi-channel parts-based cnn with improved triplet loss function

Reference 6

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Observation 38fed550-e6dd-4a28-8f3d-1d513f94dd84 · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Remote sensing image scene classification: Benchmark and state of the art

Reference 7

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Observation d38f0733-6bb2-4b21-b284-d502b3f9809a · outbound

This paper cites Learning a similarity metric discriminatively, with application to face verification.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Learning a similarity metric discriminatively, with application to face verification

Reference 8

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Observation 01464c45-02f8-4daa-a081-c3463431fa40 · outbound

This paper cites Describing textures in the wild.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Describing textures in the wild

Reference 9

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Observation bfb516be-3d0d-46e4-ad9f-a67f2da0f139 · outbound

This paper cites Multi-Head Attention: Collaborate Instead of Concatenate.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Multi-Head Attention: Collaborate Instead of Concatenate

Reference 10

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Observation f6e9cff6-7c0f-4680-9a3b-d68047f984aa · outbound

This paper cites Vision transformers need registers.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Vision transformers need registers

Reference 11

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Observation 4ca9728c-5bd6-4971-a7c8-89cf203d7b4b · outbound

This paper cites Scaling vision transformers to 22 billion pa- rameters.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Scaling vision transformers to 22 billion pa- rameters

Reference 12

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Observation c90eedad-4459-445e-8d37-d015af179ace · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Imagenet: A large-scale hierarchical image database

Reference 13

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Observation 6bbfe65f-800c-4823-be42-26f0dc4a3a02 · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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Observation a90df567-ae71-49c8-8718-f87a7d02dccc · outbound

This paper cites Diabetic retinopathy detection, 2015.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Diabetic retinopathy detection, 2015

Reference 15

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Observation f44c77ca-ec96-40d6-b631-60804d4c8e1e · outbound

This paper cites Hyperbolic vision transform- ers: Combining improvements in metric learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Hyperbolic vision transform- ers: Combining improvements in metric learning

Reference 16

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Observation bdf2baef-c4b4-4e98-aac1-73ed51acda74 · outbound

This paper cites One-shot learn- ing of object categories.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers One-shot learn- ing of object categories

Reference 17

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Observation e2fa0ccb-83e0-441e-869b-7f84b3b6db64 · outbound

This paper cites Compositional prompt tuning with motion cues for open-vocabulary video relation detection.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Compositional prompt tuning with motion cues for open-vocabulary video relation detection

Reference 18

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Observation c7613d4e-f0a6-43ea-8c2b-140b3af03e98 · outbound

This paper cites Tuning pre-trained model via moment probing.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Tuning pre-trained model via moment probing

Reference 19

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Observation 860ad188-954c-49d5-8fca-2bc260b339fd · outbound

This paper cites Visual Prompt Tuning for Test-time Domain Adaptation.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Visual Prompt Tuning for Test-time Domain Adaptation

Reference 20

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Observation 3b436858-e9b8-4d65-aa87-96054e12f293 · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Fine-grained car detection for visual census estimation

Reference 21

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Observation a55a0541-7aa2-44c9-8f3e-0aa5a2929e66 · outbound

This paper cites Vision meets robotics: The kitti dataset.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Vision meets robotics: The kitti dataset

Reference 22

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Observation e12a67d2-b294-4f1a-86cb-7d86ba1dcbbd · outbound

This paper cites Dimension- ality reduction by learning an invariant mapping.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Dimension- ality reduction by learning an invariant mapping

Reference 23

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Observation 77a4a302-746c-4f4a-b635-0e337b623cec · outbound

This paper cites E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 24

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Observation 0d863e50-386c-45ad-8be9-1ec35774e043 · outbound

This paper cites Sensitivity-aware visual parameter-efficient fine- tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Sensitivity-aware visual parameter-efficient fine- tuning

Reference 25

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Observation b48e7034-f3e5-4563-b719-2334906f7148 · outbound

This paper cites Momentum contrast for unsupervised visual repre- sentation learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Momentum contrast for unsupervised visual repre- sentation learning

Reference 26

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Observation 21759ce3-9d1f-4acc-b2d7-95ca9c39af52 · outbound

This paper cites Masked autoencoders are scalable vision learners.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Masked autoencoders are scalable vision learners

Reference 27

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Observation 0e816183-ed07-4c8d-85a9-64742e0f8b09 · outbound

This paper cites Masked autoencoders are scalable vision learners.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Masked autoencoders are scalable vision learners

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:08.814671Z

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Observation b977ee6b-06f6-4073-882e-9886bfd1031d · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:08.641837Z

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Observation a1cf976c-002a-4184-b844-dba8d49839a2 · outbound

This paper cites In Defense of the Triplet Loss for Person Re-Identification.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers In Defense of the Triplet Loss for Person Re-Identification

Reference 30

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Observation 560b98a4-436c-4765-9d16-308fd35a0479 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Parameter-efficient transfer learning for nlp

Reference 31

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Observation 2872f899-0a69-4ade-8716-682d8e621b8d · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers LoRA: Low-Rank Adaptation of Large Language Models

Reference 32

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This paper cites Visual prompt tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Visual prompt tuning

Reference 33

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Observation 98a688f4-09c0-463a-a810-a8c9ea480bcb · outbound

This paper cites Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:08.092087Z

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Observation 1df2de5d-40d4-4e26-ad80-7eedeef21e08 · outbound

This paper cites Novel dataset for fine-grained image cat- egorization: Stanford dogs.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Novel dataset for fine-grained image cat- egorization: Stanford dogs

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:07.914276Z

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Observation fa9c95c4-a588-4818-9234-1754c5517054 · outbound

This paper cites Proxy anchor loss for deep metric learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Proxy anchor loss for deep metric learning

Reference 36

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Observation 3d871d69-f182-4ea7-a7f1-83cc138b3ab0 · outbound

This paper cites Segment any- thing.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Segment any- thing

Reference 37

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Observation 45bc8ba1-88f6-4371-83c0-613f625a3b2f · outbound

This paper cites Do better imagenet models transfer better? In CVPR, pages 2661–2671,.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Do better imagenet models transfer better? In CVPR, pages 2661–2671,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:07.464445Z

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Observation 81d218bf-06ed-45c5-b984-2e6a919a41a4 · outbound

This paper cites Cross-image-attention for conditional embeddings in deep metric learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Cross-image-attention for conditional embeddings in deep metric learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:07.346793Z

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Observation f56611e8-5d4e-443d-a5cc-3975ab908f6c · outbound

This paper cites Learning multiple layers of features from tiny images.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Learning multiple layers of features from tiny images

Reference 40

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Observation f891f798-fecc-421b-b1d7-d0f7cba37879 · outbound

This paper cites M-adda: Unsuper- vised domain adaptation with deep metric learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers M-adda: Unsuper- vised domain adaptation with deep metric learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:07.150161Z

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Observation 5accebdf-bc18-481b-b06c-a6615fb4bf6d · outbound

This paper cites Learning methods for generic object recognition with invariance to pose and lighting.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Learning methods for generic object recognition with invariance to pose and lighting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:06.972304Z

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Observation 94779d85-ddee-4ab5-85b9-3ab3fb1da5b6 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 43

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Observation 5ed6b904-758c-4590-b9fe-82027cec05b6 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:56.691492Z

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Observation 2aa51a31-b82e-4ee7-8b3c-8c0ca3b93693 · outbound

This paper cites Scaling & shifting your features: A new baseline for efficient model tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Scaling & shifting your features: A new baseline for efficient model tuning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:06.804835Z

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Observation 3e4c7e2f-463e-4d39-b96d-caa93dc2f92c · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:06.649551Z

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Observation 59ffbfd6-90e6-462a-ac1c-d60ac9294238 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:56.805456Z

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Observation b04d707d-e211-40f1-9299-50d17a0d36fe · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:56.839516Z

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Observation 5ac4a9f1-e8d3-447b-889e-376cfcb5dc70 · outbound

This paper cites Decoupled Weight Decay Regularization.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Decoupled Weight Decay Regularization

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:56.876584Z

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Observation 95f17a0e-16bb-45e7-9885-5886444dbbe7 · outbound

This paper cites Exploring the limits of weakly supervised pretraining.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Exploring the limits of weakly supervised pretraining

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:06.487771Z

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Observation 7beb16f4-ca5b-4c08-ab69-95f76d6f4e25 · outbound

This paper cites dsprites: Disentanglement testing sprites dataset,.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers dsprites: Disentanglement testing sprites dataset,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:06.303365Z

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Observation 14352b09-ced2-4a7a-824e-0fef3f6720b6 · outbound

This paper cites The role of context for object detection and semantic segmentation in the wild.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers The role of context for object detection and semantic segmentation in the wild

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:06.156880Z

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Observation 39208091-70fc-40ca-8fdd-a1ede07b6a38 · outbound

This paper cites No fuss distance metric learning using proxies.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers No fuss distance metric learning using proxies

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:06.027412Z

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Observation bb22cf29-5a4f-41c4-9f1a-789e52ec2397 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Reading digits in natural images with unsupervised feature learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:05.908203Z

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Observation 62ab5cc6-0682-454f-88f7-952e83e54441 · outbound

This paper cites Toward Understanding Catastrophic Forgetting in Continual Learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Toward Understanding Catastrophic Forgetting in Continual Learning

Reference 55

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Source-reported events for the cited work

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This paper cites A visual vocabulary for flower classification.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers A visual vocabulary for flower classification

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:05.785326Z

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Observation f6063565-0416-49d9-9545-2eb9d3fe613a · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Automated flower classification over a large number of classes

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:05.648466Z

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Observation 3b3802c3-5e01-435e-ba75-31ec905dc8fc · outbound

This paper cites Cats and dogs.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Cats and dogs

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:05.455728Z

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Observation 638631f6-5034-4032-8264-c92f1063b966 · outbound

This paper cites Recall@ k surro- gate loss with large batches and similarity mixup.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Recall@ k surro- gate loss with large batches and similarity mixup

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:05.227498Z

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Observation 20375748-78a4-4755-8066-e0fde2e2b664 · outbound

This paper cites Sa 2vp: Spatially aligned-and- adapted visual prompt.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Sa 2vp: Spatially aligned-and- adapted visual prompt

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:05.127723Z

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Observation ef3f914b-6a1b-4d1e-a6ef-692c83799c53 · outbound

This paper cites AdapterHub: A Framework for Adapting Transformers.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers AdapterHub: A Framework for Adapting Transformers

Reference 61

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Observation 5f65fb55-2faf-475c-a312-8474482af3b2 · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Learning transferable visual models from natural language supervision

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:04.988040Z

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Observation 6e3b81fc-d614-44b9-a1fa-e55281b0c542 · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Learning transferable visual models from natural language supervision

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:04.875175Z

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Observation 35c46f83-d155-438c-8658-0ad72192e968 · outbound

This paper cites Beyond the deep metric learning: enhance the cross-modal matching with adversarial discriminative domain regularization.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Beyond the deep metric learning: enhance the cross-modal matching with adversarial discriminative domain regularization

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:04.783326Z

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Observation d539e582-2bee-4d2d-8936-320823f2d455 · outbound

This paper cites To- wards improved proxy-based deep metric learning via data- augmented domain adaptation.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers To- wards improved proxy-based deep metric learning via data- augmented domain adaptation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:04.701564Z

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Observation 2909f1e1-31bc-4a3a-a54c-c184117a9a4d · outbound

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DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:44:04.597578Z

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Observation 13199643-c972-4b8b-b7db-812a8a4feeae · outbound

This paper cites Non-isotropy regularization for proxy-based deep metric learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Non-isotropy regularization for proxy-based deep metric learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:04.442806Z

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Observation 1a197767-ac52-4906-8c01-364866ecb7a9 · outbound

This paper cites Neighbourhood component analysis.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Neighbourhood component analysis

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:04.262626Z

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Observation 6bf84f99-8294-4cf8-9a3a-e0987fb56e56 · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Revisiting unreasonable effectiveness of data in deep learning era

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:04.181635Z

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Observation f13ad1b3-d11b-412a-bfa7-563e41e65a92 · outbound

This paper cites Prox- ynca++: Revisiting and revitalizing proxy neighborhood com- ponent analysis.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Prox- ynca++: Revisiting and revitalizing proxy neighborhood com- ponent analysis

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:04.068765Z

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Observation 795cae06-0ac5-4079-b8e7-ccce4b753b23 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 71

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Observation 064a62a5-787a-4eb2-8d9c-ab8b2528391e · outbound

This paper cites Convolu- tional visual prompt for robust visual perception.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Convolu- tional visual prompt for robust visual perception

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:03.975586Z

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Observation 6aeaedea-3b1b-4696-85e2-880a042e876e · outbound

This paper cites Visual query tuning: Towards effective usage of intermediate representa- tions for parameter and memory efficient transfer learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Visual query tuning: Towards effective usage of intermediate representa- tions for parameter and memory efficient transfer learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:03.765035Z

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

Observation edd13ea8-5f36-4ad2-a6ef-83228107f8d0 · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:03.586310Z

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Observation bb474718-aeb9-41ef-b302-66ee56ae6d30 · outbound

This paper cites Attention is all you need.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Attention is all you need

Reference 75

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Observation e3e37212-54a7-4fa5-addf-7776594f692b · outbound

This paper cites Attention is all you need.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Attention is all you need

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:03.403516Z

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Observation e6c03e8a-3381-46c3-84c1-da10df9ed95b · outbound

This paper cites Rotation equivariant cnns for digital pathology.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Rotation equivariant cnns for digital pathology

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:03.286024Z

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Observation fe4f1499-1119-41f5-81b6-dbfaf48214b9 · outbound

This paper cites It takes two to tango: Mixup for deep metric learning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers It takes two to tango: Mixup for deep metric learning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:03.114534Z

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

Observation 71989702-ac17-4644-9177-28b6f42d1399 · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers The caltech-ucsd birds-200-2011 dataset

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:58.794340Z

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Observation 2802b62e-4073-477b-846c-863a19d2b1bd · outbound

This paper cites Adversarial cross-modal retrieval.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Adversarial cross-modal retrieval

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:02.910397Z

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Observation 121ecaa6-8624-466e-852c-2fe27aff9edb · outbound

This paper cites Adapting shortcut with normalizing flow: An efficient tuning framework for visual recognition.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Adapting shortcut with normalizing flow: An efficient tuning framework for visual recognition

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:02.688257Z

Source-reported events for the cited work

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Observation d58333dd-d6d5-4f9e-962f-b6572ea21bae · outbound

This paper cites Revisiting the Power of Prompt for Visual Tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Revisiting the Power of Prompt for Visual Tuning

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:43:59.898846Z

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Observation 4ef1c72f-3318-4e12-a6fc-a05406ccc4e6 · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Sun database: Large-scale scene recog- nition from abbey to zoo

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:02.473664Z

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 3d4a4cb1-c67b-4e3e-9a61-19ada5a00833 · outbound

This paper cites Difffit: Unlocking transferability of large diffusion models via simple parameter- efficient fine-tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Difffit: Unlocking transferability of large diffusion models via simple parameter- efficient fine-tuning

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:02.209062Z

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 217538ae-4c07-4814-821b-e1266942bfaf · outbound

This paper cites Improving visual prompt tuning for self- supervised vision transformers.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Improving visual prompt tuning for self- supervised vision transformers

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:01.993968Z

Source-reported events for the cited work

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Observation a8fa3316-c4d5-4be6-9af2-ce089c30e13f · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:59.077581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f94d2271-bb75-477f-9bd4-bd2af16650cf · outbound

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:59.110572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad13b19e-d4e7-4cbf-94b6-779e0353a143 · outbound

This paper cites MoSA: Mixture of Sparse Adapters for Visual Efficient Tuning.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers MoSA: Mixture of Sparse Adapters for Visual Efficient Tuning

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:43:59.489971Z

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 59f6711e-d790-4f7b-83bd-f9871dddc24c · outbound

This paper cites Neural Prompt Search.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Neural Prompt Search

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:59.183930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94d388f2-9bd8-437c-905e-fb0747c06713 · outbound

This paper cites Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:01.747707Z

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 811e8f89-4a3e-4436-92f7-2f1f736aeee5 · outbound

This paper cites Semantic understand- ing of scenes through the ade20k dataset.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Semantic understand- ing of scenes through the ade20k dataset

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:01.507329Z

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 2c3afc00-9444-4efb-b502-3968220f89c6 · outbound

This paper cites Following established proto- cols [19, 33, 45], we report mean accuracy across three runs with different random seeds.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Following established proto- cols [19, 33, 45], we report mean accuracy across three runs with different random seeds

Reference 93

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T12:44:00.987071Z

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 9c340b43-ab4b-4411-8eef-674f2ebf55a1 · outbound

This paper cites Details About the Experiments A.1.

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers Details About the Experiments A.1

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:01.361118Z

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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