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Paper Citation Record · LEDGER

E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2307.13770.

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

pith.paper-citation-record.v1
2307.13770 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:57:09.874642Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

10
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 49068d3d-1a59-4457-90f7-f5b0520dd710 · inbound

SMART-Vision: Survey of Modern Action Recognition Techniques in Vision cites this paper.

SMART-Vision: Survey of Modern Action Recognition Techniques in Vision E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 165

Resolution
unresolved
no resolver link, observed 2026-08-10T16:32:15.493512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:32:15.493512Z digest=sha256:0dc95fff828715004a8da7a4c3d3c686c6eea60598c0f12d8ad49bf2afcfb080

Observation 2992cd3a-5a34-457e-8f78-b939bcfa6f7e · inbound

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

Token Coordinated Prompt Attention is Needed for Visual Prompting E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-16T00:57:09.874642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T00:57:09.874642Z digest=sha256:625da4abe19dede6149f0e237a2abc27730fadc103799ee95fb81570a5acc62c

Observation 924ec6b3-02ec-4cbe-80b2-63a42601cc5f · inbound

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

Vision Graph Prompting via Semantic Low-Rank Decomposition E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:51.593986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:40:51.593986Z digest=sha256:f68ee6e0053f1876a020023f6262e521aaea68ab92be82b2b5b166ca49ff0f6d

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

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers cites this paper.

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

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T12:43:55.265616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:43:55.265616Z digest=sha256:85bcdf60e56066ad92a9ae1a09f201e694bc207612d87a292c0d254f5c95171f

Observation 583cd4d4-a83d-4a5f-9dcb-a898bbf56e1e · inbound

Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation cites this paper.

Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-15T19:52:26.979052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:52:26.979052Z digest=sha256:849a88f226b1543f227615609abfd1978668666fd41963a9a707f49c3c7aa56a

Observation 648900b7-6414-47fd-a963-7f0abcdc65d5 · inbound

Visual Instance-aware Prompt Tuning cites this paper.

Visual Instance-aware Prompt Tuning E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T18:37:35.229492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:37:35.229492Z digest=sha256:90c27d066f4750f1ed3e20b4c3f5b8790f8bbf3839de3127cc9c0cd26f62861a

Observation 5150e9b7-143c-4500-82cb-3923394dd43f · inbound

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

Visual prompting reimagined: The power of the Activation Prompts E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 76

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:45:53.883032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-10T18:52:10.770345Z digest=sha256:13e87630d638bdef481b33efd7341771814cbf4407d43208f2103f252abe3ff2

Observation 2bc504f4-aa6b-49e2-bb2d-e946d3ca3ad4 · inbound

MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation cites this paper.

MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-10T09:23:37.452774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T07:12:16.623769Z digest=sha256:1eeb9656648cfe2a778befce3dca70ec1658b69f7a5eecb884f4a3a65251a8ba

Observation 563825e5-841f-4272-a261-2725d5a0bb28 · inbound

Compared to What? Baselines and Metrics for Counterfactual Prompting cites this paper.

Compared to What? Baselines and Metrics for Counterfactual Prompting E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 98

Resolution
verified exact
arxiv_id, observed 2026-05-09T19:05:10.455666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-09T19:02:46.991897Z digest=sha256:be568601fa5df7a750d0fd6f1b0483bd225b2470e86e441fee1ea57ace74cc86

Observation 10920560-a95b-4911-897a-c43864d1ac1f · inbound

Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures cites this paper.

Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-20T04:53:05.106920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-20T04:45:53.016157Z digest=sha256:7361a40eaf98590ac8de24f9c55fc91e393776e9fb3cce32f5c6d1bbf2ebccc9

Observation a71bad50-c6a4-4139-a28f-76d034784a70 · inbound

Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models cites this paper.

Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 17

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T03:39:29.897593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-26T17:51:49.198510Z digest=sha256:f0f95425f135331ec4d05971f3724b4e4c1b9a47af5e709d17995f7ed43b524a

Observation e3ad8cc2-fbd0-4dab-9c9f-f730021edb71 · inbound

Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers cites this paper.

Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T08:59:42.692189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-26T10:43:31.650407Z digest=sha256:6325d91e6fbe8dbc3275e5e481783f197c4fb730d3bee8ae88ae7bbe927cc54c

Observation e17aaac1-8b82-4ba0-a645-e0432dc272a5 · inbound

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning cites this paper.

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T15:10:13.074455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:10:13.074455Z digest=sha256:762954e9325f9f50c255705a0bd1932b374b4005ee1964023d750b664ae6a4bc

Observation e26df252-12cd-4732-a043-8c423901b5b2 · inbound

Adapting Vision Foundation Models with Cascaded Semantics cites this paper.

Adapting Vision Foundation Models with Cascaded Semantics E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-08T14:03:48.836848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:03:48.836848Z digest=sha256:00471cd4f26478d22c97b6e94644971813e8bdb90361a338ac138557e74dc547