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Paper Citation Record · LEDGER

Exploring Visual Prompts for Adapting Large-Scale Models

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 65 inbound Pith citation observations for arXiv:2203.17274.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2203.17274 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 65 of 65 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:54:29.968030Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T00:49:19.216124Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 755e325f-8756-4f26-80a1-c00a64f254ca · inbound

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications cites this paper.

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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arxiv_id, observed 2026-05-12T21:52:10.367192Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-12T21:52:09.938550Z digest=sha256:27ebd78211850bb4feb3322fef4b3f4ab5480e5a82d585fe868b622e3063aa74

Observation 22f8bcea-76f0-4749-9573-44fb78f5fbc3 · inbound

Subgraph-level Universal Prompt Tuning cites this paper.

Subgraph-level Universal Prompt Tuning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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arxiv_id, observed 2026-05-24T03:38:50.032879Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-24T03:37:59.957481Z digest=sha256:2157f810b1ab6e9e607086c267792fb5d62233d6a1441ecf35e15d40c1bbe95f

Observation fcea3cc7-99b8-40a1-b527-79e7f7f54ad0 · inbound

Robust Adaptation of Foundation Models with Black-Box Visual Prompting cites this paper.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Exploring Visual Prompts for Adapting Large-Scale Models

Reference 6

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arxiv_id, observed 2026-05-23T23:23:36.348169Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-23T23:23:03.562550Z digest=sha256:a167ed7c28d554c2bbce4a2f76eac97c11584be2390ec1184496181510dbf301

Observation 2ce0235c-986a-4280-a380-bb6f62527311 · inbound

Prompting the Unseen: Detecting Hidden Backdoors in Black-Box Models cites this paper.

Prompting the Unseen: Detecting Hidden Backdoors in Black-Box Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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no resolver link, observed 2026-08-12T20:37:30.314630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:37:30.314630Z digest=sha256:407249a7ae1074b0b680709ed3ca25d1ba06a9b713871c459b82527414996802

Observation 8a0df5ab-e561-4cd1-9d06-35d6dc928d38 · inbound

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing cites this paper.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Exploring Visual Prompts for Adapting Large-Scale Models

Reference 3

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source=pdf_text observed=2026-08-12T17:35:41.905313Z digest=sha256:5d12c2dd97a7b0045f2cbd41d4332073bb64a287ba2c5a8776e75a33cb6234ca

Observation 8d7aa66b-5300-4825-8282-f41aa7eb22ca · inbound

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models cites this paper.

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 5

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source=pdf_text observed=2026-08-12T16:52:27.619224Z digest=sha256:29483b501ebcb3b9ed8d0724a7451ba0b53458e31ad54e2a21338005d1d01d17

Observation 984c5862-a7fe-48fc-8962-af041bad7a2c · inbound

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning cites this paper.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 50

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source=pdf_text observed=2026-08-12T15:02:32.410155Z digest=sha256:c2d7e07ded87c647bbc963ed0652bd49e79cad7e18a6c1323ee44b2bf8f7af37

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

Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation cites this paper.

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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source=pdf_text observed=2026-08-12T10:24:01.141578Z digest=sha256:f1e8613bb43ba70d82285d15a72ce77b2a74472bda237f21220366ae26ba2866

Observation 1a167833-5801-41e8-8b56-083c57f186d2 · inbound

Visual Modality Prompt for Adapting Vision-Language Object Detectors cites this paper.

Visual Modality Prompt for Adapting Vision-Language Object Detectors Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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source=pdf_text observed=2026-08-12T05:14:07.584130Z digest=sha256:331361ccadb7c1f07c6586a925261f4c9dd8cda92e677c1d5b78f31b0ddf55f8

Observation 19f27a73-ed13-48b0-ada4-998d62d24407 · inbound

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents cites this paper.

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Exploring Visual Prompts for Adapting Large-Scale Models

Reference 47

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source=pdf_text observed=2026-08-11T14:57:26.839693Z digest=sha256:1428df6334e4e97c7bc6da931e5c3d6aad44ed897474fee78e4df64893b1b13b

Observation 318891e4-d2a2-4af3-9dd7-46da8a2293f2 · inbound

PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts cites this paper.

PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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source=arxiv_source observed=2026-08-11T14:08:13.881147Z digest=sha256:080b98ae70deaeaba77cdd646481fba160cf88996866b05424b3ae1f31bfdf35

Observation 9c8dfd96-8819-4142-820c-10de0f7150ae · inbound

Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image Classification cites this paper.

Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image Classification Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1993

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no resolver link, observed 2026-08-11T10:18:41.540772Z

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source=pdf_text observed=2026-08-11T10:18:41.540772Z digest=sha256:6ea1b1736ba1553f922db2a0f615d1aac2330e8f805e968bc46f4e8fe670a239

Observation 38476415-6dae-43eb-8773-bed6e9c58852 · inbound

Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning cites this paper.

Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 33

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source=pdf_text observed=2026-08-11T06:02:33.387616Z digest=sha256:b55e60069d52e1080ca4ebcbd0e798bb738878a4d00af5ab6a3ce5e30bab7d68

Observation d878a7b6-5845-47db-b99b-6ce4702cdc54 · inbound

ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning cites this paper.

ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 5

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source=arxiv_source observed=2026-08-11T00:47:52.355230Z digest=sha256:4d015fd768efee00ba0cacb1d4b67afb8369997824fdb01568a5085655c99b03

Observation bfdd3c2a-a566-4219-8d85-e85f49a2d762 · inbound

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models cites this paper.

ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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no resolver link, observed 2026-08-10T18:40:27.033379Z

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source=pdf_text observed=2026-08-10T18:40:27.033379Z digest=sha256:5585418263c62517030c01f479d6f07af654258299281c565de0962da28e3f64

Observation a43c9c54-c05f-4259-becf-cb10bc2502e1 · inbound

Parameter-Efficient Fine-Tuning for Foundation Models cites this paper.

Parameter-Efficient Fine-Tuning for Foundation Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 96

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source=pdf_text observed=2026-08-10T15:38:03.144758Z digest=sha256:333840027444b00324e88684d30297fc78f5d03a3ecab4a01b0439b14c86f94e

Observation 4e785651-5e9e-4fa0-adec-4410538c4b2c · inbound

SelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation cites this paper.

SelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2022

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source=pdf_text observed=2026-08-10T15:24:42.255628Z digest=sha256:c981d08fcdfc5d1a2d7ce4337395ef8890754bb078cfbbf6a7f29055896bbb47

Observation 2b455ae0-df25-4d15-b227-89e0894fc26d · inbound

DynaPrompt: Dynamic Test-Time Prompt Tuning cites this paper.

DynaPrompt: Dynamic Test-Time Prompt Tuning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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source=pdf_text observed=2026-08-10T13:52:34.520436Z digest=sha256:965a507bcd190d8e558e954208329bfe75deea0675bdc3cec6435650898313c0

Observation 509017f4-7d0c-47fd-8829-f6d09806e771 · inbound

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation cites this paper.

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Exploring Visual Prompts for Adapting Large-Scale Models

Reference 5

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source=arxiv_source observed=2026-08-09T17:23:25.342323Z digest=sha256:2f29574a1e2567a8f9bcddcb88c66b796dd257795656db19ecbf1a14602a5135

Observation daa4f87e-5307-4586-b804-5939b38108c3 · inbound

Revisiting the Auxiliary Data in Backdoor Purification cites this paper.

Revisiting the Auxiliary Data in Backdoor Purification Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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source=pdf_text observed=2026-08-08T13:30:03.452514Z digest=sha256:c062beab6f29c6ce7edc526801221db58974105597c79d865e6eec57a25546d1

Observation 7a7c85a5-a64e-4606-ad42-67aa423d56ff · inbound

CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey cites this paper.

CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey Exploring Visual Prompts for Adapting Large-Scale Models

Reference 4

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source=pdf_text observed=2026-08-16T11:54:29.968030Z digest=sha256:f871a8a883b1be63bbdc0bfa92b92c5539e3b390d114c5f0c49f26ed1168543b

Observation 2de664f1-ea97-44ef-b6e3-9d58ca17034b · inbound

E-InMeMo: Enhanced Prompting for Visual In-Context Learning cites this paper.

E-InMeMo: Enhanced Prompting for Visual In-Context Learning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 19

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source=pdf_text observed=2026-08-16T10:29:47.416431Z digest=sha256:b6de1671294d2a55aa96ff7861b62cfd56e5a8d108035c1f7210f7a728da6867

Observation aa6135e7-6e1a-435c-88a2-d2fe1a7406e2 · inbound

BARIS: Boundary-Aware Refinement with Environmental Degradation Priors for Robust Underwater Instance Segmentation cites this paper.

BARIS: Boundary-Aware Refinement with Environmental Degradation Priors for Robust Underwater Instance Segmentation Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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source=pdf_text observed=2026-08-16T05:52:10.682829Z digest=sha256:3a50a96ebdf563fd929c56afa6ba75461cdcde03cb09e85c77c8a96afaebcd16

Observation d7c78070-7bd2-49cf-a316-115a5e53901c · inbound

Diff-Prompt: Diffusion-Driven Prompt Generator with Mask Supervision cites this paper.

Diff-Prompt: Diffusion-Driven Prompt Generator with Mask Supervision Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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source=pdf_text observed=2026-08-16T05:10:32.892425Z digest=sha256:57a97fc76f3518e5e4d14cc0aa4e84d5353502f2668b2e72b79e02b8194a6605

Observation c7df85be-5a86-4fe2-8fc5-467bda7132ec · inbound

Token Coordinated Prompt Attention is Needed for Visual Prompting cites this paper.

Token Coordinated Prompt Attention is Needed for Visual Prompting Exploring Visual Prompts for Adapting Large-Scale Models

Reference 3

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source=arxiv_source observed=2026-08-16T00:57:09.832742Z digest=sha256:4b763559fc80d23c599510c4fb7ef21083fefb4e65a5a9a10d0c1b1178cd20e7

Observation 2345bfd0-d435-4d69-b7fd-2d4ea24f0897 · inbound

Vision Graph Prompting via Semantic Low-Rank Decomposition cites this paper.

Vision Graph Prompting via Semantic Low-Rank Decomposition Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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source=pdf_text observed=2026-08-15T23:40:51.562082Z digest=sha256:9e4c80b30acb7bac8d414cf9d9b09d02a6e485010a6e216305eaf0fe62c6133c

Observation 647a6ef4-9f69-4651-94a4-f9a0d0f5833d · inbound

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation cites this paper.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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source=pdf_text observed=2026-08-15T22:45:19.580426Z digest=sha256:9daa3557259cdaba6514215398eb38d464dbfc4301aa8c8584c9645e25774efd

Observation 3d7ad84a-6acb-4be4-80a3-28519cb14c96 · inbound

Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis cites this paper.

Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis Exploring Visual Prompts for Adapting Large-Scale Models

Reference 37

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source=pdf_text observed=2026-08-15T21:39:14.061201Z digest=sha256:d9a06aa300f460547e5014258e7c78061f42bf41f2695250e1a5cab7117a3f4e

Observation a3177020-49a8-40e6-b885-eefd51385243 · inbound

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding cites this paper.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Exploring Visual Prompts for Adapting Large-Scale Models

Reference 3

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source=pdf_text observed=2026-08-07T15:27:21.565626Z digest=sha256:f066277eec9e71227578c4e0af86ac160f251dfc9058302b27006ea8660e6c65

Observation b2f9bdee-b5ef-488f-a24b-645e8e0715c4 · inbound

Dual-Path Stable Soft Prompt Generation for Domain Generalization cites this paper.

Dual-Path Stable Soft Prompt Generation for Domain Generalization Exploring Visual Prompts for Adapting Large-Scale Models

Reference 59

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source=pdf_text observed=2026-08-07T14:29:37.658386Z digest=sha256:59300bdf21e28a6a11975ed926edcf5c8196991eadd1ab0237437c0efbdb236c

Observation f41d087e-c57c-4113-99e1-ae1913b0af76 · inbound

MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning cites this paper.

MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 3

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source=pdf_text observed=2026-08-07T13:58:03.278749Z digest=sha256:9d5f4ed0b6cbdec5beb2bffc852d353364379e70f9c9a9c871e611c506c1e386

Observation ef5adab9-0979-4da1-a0f1-c8cbb3a616de · inbound

Model Reprogramming Demystified: A Neural Tangent Kernel Perspective cites this paper.

Model Reprogramming Demystified: A Neural Tangent Kernel Perspective Exploring Visual Prompts for Adapting Large-Scale Models

Reference 4

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source=arxiv_source observed=2026-08-07T12:09:48.866213Z digest=sha256:c19e224bb5fc220c50da495cfeb38b527cf5fffd0ca680b934a8cd7f8886dea3

Observation 0a91bcad-9e1b-4a6a-8e5e-861009a9bf32 · inbound

Exploring Visual Prompting: Robustness Inheritance and Beyond cites this paper.

Exploring Visual Prompting: Robustness Inheritance and Beyond Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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source=arxiv_source observed=2026-08-07T05:52:38.655882Z digest=sha256:d51c61dceda28acc23823af760a0e02a98f49c180e1585deff603f059791642e

Observation 355dc0b5-766f-4a70-8abe-601b140d994b · inbound

CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization cites this paper.

CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2023

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source=pdf_text observed=2026-08-07T05:40:28.964278Z digest=sha256:5f1b82dbf43ff7a817ea28eead0bb1d954aab9a8b81ac01a4688de498c3d9f16

Observation f059a6d5-a6e6-4e84-a65e-8b9f95e79385 · inbound

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models cites this paper.

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 64

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source=pdf_text observed=2026-08-07T00:14:15.210742Z digest=sha256:39d6bf3c8e86a95b652f065b23ec68671db1167a685cbb5053c83f06d82b7cf4

Observation 4201aed3-822e-4e97-8a35-cf388bf94315 · inbound

Generalizing vision-language models to novel domains: A comprehensive survey cites this paper.

Generalizing vision-language models to novel domains: A comprehensive survey Exploring Visual Prompts for Adapting Large-Scale Models

Reference 87

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no resolver link, observed 2026-08-06T23:20:44.918777Z

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source=pdf_text observed=2026-08-06T23:20:44.918777Z digest=sha256:9d01ed28b15ade0368b7a47c6dc45c0f760eeaa2988c39beaa6252e80450ed2d

Observation a8a31ae4-53dd-4930-936c-bc8d7851bbba · inbound

Visual Textualization for Image Prompted Object Detection cites this paper.

Visual Textualization for Image Prompted Object Detection Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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source=pdf_text observed=2026-08-06T21:37:03.071096Z digest=sha256:79243ff613792883fa43ed0bcfb8f13ee003ca70d74d222722a86377aa6e28a4

Observation f44d106a-f5ca-47ae-bd2e-9146376fb124 · inbound

Visual Instance-aware Prompt Tuning cites this paper.

Visual Instance-aware Prompt Tuning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 4

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source=pdf_text observed=2026-08-06T18:37:35.090755Z digest=sha256:4953d7018b47af0aec50ead806201e8577c8e77f87518767df12b740525a2f8e

Observation a810cc3f-b520-4e8b-837c-051629199850 · inbound

DynImg: Key Frames with Visual Prompts are Good Representation for Multi-Modal Video Understanding cites this paper.

DynImg: Key Frames with Visual Prompts are Good Representation for Multi-Modal Video Understanding Exploring Visual Prompts for Adapting Large-Scale Models

Reference 3

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source=pdf_text observed=2026-08-06T15:34:44.361412Z digest=sha256:969853cc46fdc194affa4f2f1ce75afa7e86bc87269b103b03264ff71ac1874f

Observation d48289a5-6a7c-46c4-803c-462a4ce5110f · inbound

DepthDark: Robust Monocular Depth Estimation for Low-Light Environments cites this paper.

DepthDark: Robust Monocular Depth Estimation for Low-Light Environments Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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source=pdf_text observed=2026-08-06T14:44:24.547505Z digest=sha256:d68d468a545331241b0983aa4ccb0d835a7ce96010aeebae52c32e67d98dd1b2

Observation 7b060917-d2fc-4852-a4e9-0c929326980c · inbound

ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking cites this paper.

ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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no resolver link, observed 2026-08-15T18:00:25.051633Z

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source=pdf_text observed=2026-08-15T18:00:25.051633Z digest=sha256:994866fefdaaf55d5743ab8737327df5499fa98054e582a661b6c8455c23500b

Observation 535c30e0-6bf9-4295-9d97-e9e7b4d222eb · inbound

Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning cites this paper.

Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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no resolver link, observed 2026-08-06T12:42:01.672805Z

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source=pdf_text observed=2026-08-06T12:42:01.672805Z digest=sha256:21b6642a82d46a8fe3f25b467d33042e780a38c0e48ca3540f8407205ae42eee

Observation 94857fbf-9c66-4aef-9382-bfa295a1ce11 · inbound

Twistronics and moir\'e superlattice physics in 2D transition metal dichalcogenides cites this paper.

Twistronics and moir\'e superlattice physics in 2D transition metal dichalcogenides Exploring Visual Prompts for Adapting Large-Scale Models

Reference 72

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source=pdf_text observed=2026-08-06T05:32:05.603281Z digest=sha256:097bbec3c552a91838ebffddb9d16dd101cf113a58a7517b1ba227a695cc0ece

Observation 7148a55f-e9ee-4945-82d2-a17bde025b00 · inbound

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design cites this paper.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Exploring Visual Prompts for Adapting Large-Scale Models

Reference 64

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no resolver link, observed 2026-08-05T21:01:26.913858Z

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source=pdf_text observed=2026-08-05T21:01:26.913858Z digest=sha256:b736653f9a3d9f69c1704de665017a2da1a739aebd5bc1b9e63045af0050c1eb

Observation d6ee39eb-6a72-406f-b9a7-db5ef07de65b · inbound

Polarization-Resolved Chlorophyll Imaging for Non-Invasive Plant Tissue Assessment Using a Silicon-Rich Nitride Metalens Array cites this paper.

Polarization-Resolved Chlorophyll Imaging for Non-Invasive Plant Tissue Assessment Using a Silicon-Rich Nitride Metalens Array Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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source=pdf_text observed=2026-08-05T18:45:08.262623Z digest=sha256:39e2fd913d680ed3096c9821e6fa0c811c5eaf68750490e03c5d54e71dc37385

Observation f707f0b5-ef9f-4cda-befb-c3de0ebc6f3d · inbound

CLIPSym: Delving into Symmetry Detection with CLIP cites this paper.

CLIPSym: Delving into Symmetry Detection with CLIP Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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source=pdf_text observed=2026-08-05T18:48:34.686795Z digest=sha256:5c6ed7cb21f92f80b3f463e3d4171cd4ebf57bb850b934b6f8c520e6631f2b9c

Observation a5d17cd1-77ae-4eec-b466-b453eb082bc1 · inbound

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis cites this paper.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Exploring Visual Prompts for Adapting Large-Scale Models

Reference 65

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source=pdf_text observed=2026-08-05T13:45:43.985537Z digest=sha256:a4258db0995c5fa820d724c7018a1cd4a3c86ae4865259c34fd57a1c36d1b802

Observation 241f9d26-79d5-4ac8-af64-68c4e92693a7 · inbound

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA cites this paper.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Exploring Visual Prompts for Adapting Large-Scale Models

Reference 11

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source=pdf_text observed=2026-08-05T10:55:55.673736Z digest=sha256:be28a87aa5a6fb637ab60e8ed621db96b5fd52e94955e5164e871a9dfeb8c042

Observation 1128d73f-a5d9-4deb-8e6a-35935c20d013 · inbound

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning cites this paper.

Few-Shot Query Intent Detection via Relation-Aware Prompt Learning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 34

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source=pdf_text observed=2026-08-05T05:21:30.834999Z digest=sha256:28df6d0ef013bd88e10c739dde94924f841af08a28b857f375dae6b782ba43ae

Observation d1f09176-d956-4c63-863b-08ddb346d4e6 · inbound

AttriPrompt: Dynamic Prompt Composition Learning for CLIP cites this paper.

AttriPrompt: Dynamic Prompt Composition Learning for CLIP Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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no resolver link, observed 2026-08-05T04:50:27.627722Z

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source=pdf_text observed=2026-08-05T04:50:27.627722Z digest=sha256:d775726cd482ece48cfe50ae4ac4aaa0d7c900dca816546d00aca792e055ee8e

Observation 48c90f39-0524-4b32-a760-c8df16b39471 · inbound

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting cites this paper.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Exploring Visual Prompts for Adapting Large-Scale Models

Reference 17

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source=pdf_text observed=2026-08-04T12:41:48.171666Z digest=sha256:cc056879d81901089c8661170ae31cb0046d4bcfa0c614766a6c5fdd12233a97

Observation 092ca34d-4bbb-4d74-a258-365ccec9222e · inbound

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation cites this paper.

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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no resolver link, observed 2026-08-02T21:07:44.774219Z

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source=pdf_text observed=2026-08-02T21:07:44.774219Z digest=sha256:0de6512e1dc92ce6bf1b12ca8fb3d18e5470c01e3baf82f5f0686260e5c76276

Observation 7971877d-d16b-49bd-a150-b07fa417ab01 · inbound

Visual prompting reimagined: The power of the Activation Prompts cites this paper.

Visual prompting reimagined: The power of the Activation Prompts Exploring Visual Prompts for Adapting Large-Scale Models

Reference 50

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verified exact
arxiv_id, observed 2026-05-10T23:45:53.794408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T18:52:10.770345Z digest=sha256:c71fd185b3fcfd42020c1faf7c85333fb7edb187667c998d3ef4acca69273da8

Observation 201de585-a328-4a43-9536-40209c69348f · inbound

Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models cites this paper.

Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 69

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verified exact
arxiv_id, observed 2026-05-11T15:16:11.008917Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-09T20:20:20.864444Z digest=sha256:d9d0ad6abced708a562aace2e499c2b76b6490edf5e5835b2cd5252f2ae9ce26

Observation ec967b38-6f25-4f53-bf50-3cf01355e7cf · inbound

Plug-and-play Class-aware Knowledge Injection for Prompt Learning with Visual-Language Model cites this paper.

Plug-and-play Class-aware Knowledge Injection for Prompt Learning with Visual-Language Model Exploring Visual Prompts for Adapting Large-Scale Models

Reference 11

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metadata mismatch
arxiv_id, observed 2026-05-11T16:36:09.441123Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-09T15:52:06.698536Z digest=sha256:df9f43502bad4b5d7ef03aa4a4b646c514359f4d83fefc6340573b177a3f475f

Observation 76d31edb-a4dc-4c31-a012-6a4a56115b0a · inbound

Efficient Prompt Learning for Traffic Forecasting cites this paper.

Efficient Prompt Learning for Traffic Forecasting Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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metadata mismatch
arxiv_id, observed 2026-05-12T08:06:26.680493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-12T01:20:37.192907Z digest=sha256:abd51dd14454c65842013eccd1a3b6224294a70a65323318ee7dcced0f43677b

Observation 49a774fc-4d22-4a12-8921-c6917544589a · inbound

Thermal-Det: Language-Guided Cross-Modal Distillation for Open-Vocabulary Thermal Object Detection cites this paper.

Thermal-Det: Language-Guided Cross-Modal Distillation for Open-Vocabulary Thermal Object Detection Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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verified exact
arxiv_id, observed 2026-05-12T03:26:19.261914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-12T03:25:13.709254Z digest=sha256:b416173ad0d974b855ad010d97bcb5e58fefb4c81d196fc3267701b2890fdb84

Observation fb7d82dc-8cbe-4dba-aa84-4215614ca838 · inbound

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models cites this paper.

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 66

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verified exact
arxiv_id, observed 2026-05-20T14:23:21.477774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-20T14:20:49.278545Z digest=sha256:5080f489330a433b9b1834077ca40fe3d097b736620dd939767d1a2715cc4245

Observation 05ef2002-a4ca-4796-933e-7f588ddeefca · inbound

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning cites this paper.

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

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verified exact
arxiv_id, observed 2026-07-01T19:56:11.303547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-28T21:52:23.150188Z digest=sha256:d31888f2105c8237953dd4690a95daefbd240fa9794a68dfabe80333b05046a8

Observation f0cfe145-e2b6-4d75-98ab-eca2f337e9dc · inbound

Latent Diffusion Pretraining for Crystal Property Prediction cites this paper.

Latent Diffusion Pretraining for Crystal Property Prediction Exploring Visual Prompts for Adapting Large-Scale Models

Reference 44

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metadata mismatch
arxiv_id, observed 2026-06-28T19:32:34.856001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-06-28T19:30:38.954152Z digest=sha256:6bae7ce37f0165d2133a2dd5f8b8a9d1fff41568815da79abef294e502932262

Observation 718df71c-c640-413d-8ccd-de9d456989a1 · inbound

Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training cites this paper.

Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training Exploring Visual Prompts for Adapting Large-Scale Models

Reference 13

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verified exact
arxiv_id, observed 2026-07-03T09:07:48.149675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T10:30:51.490047Z digest=sha256:9354fd2413b4f62ffdc7110163e2ace9707b4d807b95354c84fa98e69e50afc4

Observation b409a6b4-2b1c-469c-a19c-41e9306614ba · inbound

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception cites this paper.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:49:19.219783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-26T20:54:48.203293Z digest=sha256:222cd8972ad865dab3f22c3bb6e32dc858e325866e9bb751d66caf00ac672640

Observation 6ff0bf58-5ded-477b-9455-b7faf87feed8 · inbound

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models cites this paper.

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

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metadata mismatch
arxiv_id, observed 2026-06-30T07:04:21.640534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-30T06:58:28.215741Z digest=sha256:cf215c3e94252569389bd070aa69ff267142cafa6c0adf209e53ba4a836f0265

Observation 085e645b-ba73-4dbc-98ac-2d97d35e2bfc · inbound

Visual prompt engineering for video models cites this paper.

Visual prompt engineering for video models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 24

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no resolver link, observed 2026-08-01T02:13:12.362932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T02:13:12.362932Z digest=sha256:f55c8d55256d491c7b7e907a48e6527374935c1ddb892fe8d402094bd83cfc08

Observation 5a7ef476-59b4-47a5-a3ba-43babdb96744 · inbound

Scaling Representation Diversity: Modulated Attention and Reconstructive Regularization for Visual Grounding cites this paper.

Scaling Representation Diversity: Modulated Attention and Reconstructive Regularization for Visual Grounding Exploring Visual Prompts for Adapting Large-Scale Models

Reference 95

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no resolver link, observed 2026-08-16T00:09:18.472945Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T00:09:18.472945Z digest=sha256:e36c58767cc446c89c6d622cc5e0ac6a4213827c9d5cf568cfb3c517aaf34ab7