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Paper Citation Record · LEDGER

Unified Vision and Language Prompt Learning

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2210.07225.

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

pith.paper-citation-record.v1
2210.07225 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:24:33.625715Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:56:11.264751Z

Reference resolution

0 of 0 outbound references displayed

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Outbound references

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Pith citing papers

Observation 9b391797-2dda-4b4e-bf9b-be54cf8708e6 · inbound

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

Robust Adaptation of Foundation Models with Black-Box Visual Prompting Unified Vision and Language Prompt Learning

Reference 12

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verified exact
arxiv_id, observed 2026-05-23T23:23:36.362418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-23T23:23:03.562550Z digest=sha256:b313d02acfd0de20a2fb4ed8ae436fe89eda801f56b403d4981a970698266968

Observation 7fa3b420-754e-499b-9489-2fb4b47b4d9c · inbound

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning cites this paper.

Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning Unified Vision and Language Prompt Learning

Reference 95

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no resolver link, observed 2026-08-12T19:14:24.286924Z

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source=pdf_text observed=2026-08-12T19:14:24.286924Z digest=sha256:504aff05586e27e7cebf771f3df6ecffde613ca2d4553cb23ed11e5fb506ee4d

Observation 2fae7ffd-2efb-4a74-832f-e2492235e3c3 · inbound

A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal Adapter cites this paper.

A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal Adapter Unified Vision and Language Prompt Learning

Reference 36

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no resolver link, observed 2026-08-11T17:27:49.486626Z

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source=arxiv_source observed=2026-08-11T17:27:49.486626Z digest=sha256:9743b9210b0590b79312c3572acfc5429fc8676c5e2f7135738e06ff5b820a8e

Observation 5004d9f0-efa5-4d6c-956a-19b78d0446eb · inbound

Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval cites this paper.

Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval Unified Vision and Language Prompt Learning

Reference 57

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no resolver link, observed 2026-08-11T15:20:47.281351Z

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source=arxiv_source observed=2026-08-11T15:20:47.281351Z digest=sha256:bae1c48c155e8baa3036f9d011105f0282d25fa97123824cec92e579d9b63dc1

Observation 1d8a6df2-3b78-4d5b-b138-bac6621499c6 · inbound

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

Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents Unified Vision and Language Prompt Learning

Reference 49

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no resolver link, observed 2026-08-11T14:57:26.850243Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:57:26.850243Z digest=sha256:a870d14066eab72a93b42b76387abccabb94355c0d89d43616df75642118317a

Observation d83aaa4e-80f9-4c28-a96b-cb41c20b476a · inbound

Differentiable Prompt Learning for Vision Language Models cites this paper.

Differentiable Prompt Learning for Vision Language Models Unified Vision and Language Prompt Learning

Reference 70

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no resolver link, observed 2026-08-10T22:54:52.244277Z

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source=pdf_text observed=2026-08-10T22:54:52.244277Z digest=sha256:7b38e5d0f4834ef86e3b8071d7e0f2b6a47849a8bab9f506349bcf1a11219331

Observation 81e7903e-4ab9-4d2c-95f2-8fe5605cacde · 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 Unified Vision and Language Prompt Learning

Reference 61

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source=pdf_text observed=2026-08-10T18:40:27.352337Z digest=sha256:eb3e2d2a9d678510378c9cad5cbdd66930907624da99442f6a8b01c1de96dac8

Observation 1f316495-d2d1-41c2-a895-ee29292b7a3f · inbound

INT: Instance-Specific Negative Mining for Task-Generic Promptable Segmentation cites this paper.

INT: Instance-Specific Negative Mining for Task-Generic Promptable Segmentation Unified Vision and Language Prompt Learning

Reference 32

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no resolver link, observed 2026-08-09T22:41:21.249653Z

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source=pdf_text observed=2026-08-09T22:41:21.249653Z digest=sha256:be7d40027f17add0db30cb1a6ca22fc6974eb5ee92904d1e0abdfce3c4ff4ce0

Observation 8c07e3f0-3ee9-462b-a1af-89adab118d97 · inbound

From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection cites this paper.

From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection Unified Vision and Language Prompt Learning

Reference 40

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source=arxiv_source observed=2026-08-15T20:24:33.625715Z digest=sha256:1f107e59b7a739de8751c5c4b21f0a5b530f87d1328567bc855b3a3b72c07f6a

Observation a4721250-0fee-4c53-9569-210c1669cab5 · inbound

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

Generalizing vision-language models to novel domains: A comprehensive survey Unified Vision and Language Prompt Learning

Reference 84

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no resolver link, observed 2026-08-06T23:20:44.558520Z

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source=pdf_text observed=2026-08-06T23:20:44.558520Z digest=sha256:9e2cfd4bfced5c85c78a5658493e65bf278b1ea6eb99ab4d16307c4773c8194d

Observation 238e3490-284d-4bb2-961f-47d8d62720ee · inbound

Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models cites this paper.

Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models Unified Vision and Language Prompt Learning

Reference 15

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source=pdf_text observed=2026-08-06T18:27:46.914058Z digest=sha256:daf0f070d3651d72e838305ea835c0575fec96671115c7caac13aa1e04c5cdaa

Observation 4e1bf780-8215-4fab-ba9e-e3142fd70d09 · inbound

One Last Attention for Your Vision-Language Model cites this paper.

One Last Attention for Your Vision-Language Model Unified Vision and Language Prompt Learning

Reference 54

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source=pdf_text observed=2026-08-06T15:42:16.472212Z digest=sha256:0fb1bf1ea976018fab523d360b6b2f8642b045fdcca426624905a7358e08e9c3

Observation 39cf9e76-117e-4977-ac08-f79d7f62f868 · inbound

HOLa: Zero-Shot HOI Detection with Low-Rank Decomposed VLM Feature Adaptation cites this paper.

HOLa: Zero-Shot HOI Detection with Low-Rank Decomposed VLM Feature Adaptation Unified Vision and Language Prompt Learning

Reference 56

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source=pdf_text observed=2026-08-06T15:36:17.773682Z digest=sha256:c17ed858a062a8244402f02f0eb0eb645a07ff977f8b7d330acc2b86591caab3

Observation 624eca69-ece9-48ad-a3fe-6ee505763a9a · 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 Unified Vision and Language Prompt Learning

Reference 51

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source=pdf_text observed=2026-08-06T15:34:44.496621Z digest=sha256:99e5ac028e4640b3f939f534f0c3f8aa92cbc625ddc922dcebedc43c03ab20b9

Observation 14f5836f-5f9b-4a54-9b2e-280aa943cf85 · inbound

ProPy: Building Interactive Prompt Pyramids upon CLIP for Partially Relevant Video Retrieval cites this paper.

ProPy: Building Interactive Prompt Pyramids upon CLIP for Partially Relevant Video Retrieval Unified Vision and Language Prompt Learning

Reference 44

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no resolver link, observed 2026-08-05T16:05:18.776777Z

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source=arxiv_source observed=2026-08-05T16:05:18.776777Z digest=sha256:c1673fb48ab49f90af69d23e1488b8c517d6829b2a6fc8073673abd9d7626c52

Observation 430a400d-024f-44b2-aaed-d4f41bdcf8ee · 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 Unified Vision and Language Prompt Learning

Reference 66

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no resolver link, observed 2026-08-02T21:07:50.961211Z

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source=pdf_text observed=2026-08-02T21:07:50.961211Z digest=sha256:0b2351e5831701ab762f3acff69f93f03e655cd130660354d1a0d8ffe738708d

Observation b8f107ab-92ba-40cf-8cca-ad6cd4da042d · 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 Unified Vision and Language Prompt Learning

Reference 13

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metadata mismatch
arxiv_id, observed 2026-05-11T16:36:09.434253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-09T15:52:06.698536Z digest=sha256:a3910d6f106805ae56ce21ca5a3d272e86a29e51c9dffb6a2813ed4baed9e601

Observation 9dbc0ea5-6f51-4c6a-a557-f63ee02a9a45 · inbound

LAGO: Language-Guided Adaptive Object-Region Focus for Zero-Shot Visual-Text Alignment cites this paper.

LAGO: Language-Guided Adaptive Object-Region Focus for Zero-Shot Visual-Text Alignment Unified Vision and Language Prompt Learning

Reference 11

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verified exact
arxiv_id, observed 2026-05-12T01:46:14.753399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-12T01:38:09.299209Z digest=sha256:9a6ee0d6638a28634cfe74b109769c8274e183b369031d19616e41366cc0e06e

Observation 1f3fa981-2b57-451d-8e51-02776867f051 · inbound

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

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning Unified Vision and Language Prompt Learning

Reference 71

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arxiv_id, observed 2026-07-01T19:56:11.266427Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T21:52:23.150188Z digest=sha256:dbbaa3c0dfda8e3dd24f0f4d239170915c865e8d08ad8c37726097a6bbc980b4

Observation b6ab8ff8-cd44-49a3-838d-280f7150b7ac · inbound

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes cites this paper.

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes Unified Vision and Language Prompt Learning

Reference 50

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source=pdf_text observed=2026-07-13T01:03:27.721212Z digest=sha256:75f8409b748c5a9bfc5a12c2e7208807659a90766725857623a5c29ede34e54f

Observation 8b1b1f59-2338-4732-a32c-6c7cbda10091 · inbound

MuRA: Multi-Rank Adaptation for Efficient and Effective Test-Time Vision-Language Generalization cites this paper.

MuRA: Multi-Rank Adaptation for Efficient and Effective Test-Time Vision-Language Generalization Unified Vision and Language Prompt Learning

Reference 38

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source=pdf_text observed=2026-08-05T10:32:23.244452Z digest=sha256:e11667897dc4e1c6ee4f990eefe40d677451215074fd96414002dfe60427be39