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Paper Citation Record · LEDGER

Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2210.08773.

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

pith.paper-citation-record.v1
2210.08773 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:21:45.111786Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T07:25:28.419990Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3c1f6acb-6bf3-4fcc-ac9f-1485723f535a · inbound

MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models cites this paper.

MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-10T20:37:01.977401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T20:37:01.617345Z digest=sha256:45c9bb5b267bd3839cca77f5121a12aae52db0a37acc11e4a28571670d086a62

Observation 38a70ab9-d8cb-4f4f-afd1-4c0e5a1b75f7 · inbound

The ART of Composition: Attention-Regularized Training for Compositional Visual Grounding cites this paper.

The ART of Composition: Attention-Regularized Training for Compositional Visual Grounding Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-23T07:25:28.422147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T07:24:01.527093Z digest=sha256:05ce196588c5c92520f07aeb332ab69a1387ad8d9ad23a76db09785fda0a4492

Observation cdc83392-a7dd-44c7-aef0-d7180cef1400 · inbound

GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance cites this paper.

GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T14:21:45.111786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:21:45.111786Z digest=sha256:cee604c1e62470a9de506ff0b05ce0bd5bd6e3ec1b5c7847a0dd6509fd9e94d4

Observation 33457ebf-634d-4ef1-b733-f6263fcfa8da · inbound

FREE: Fast and Robust Vision Language Models with Early Exits cites this paper.

FREE: Fast and Robust Vision Language Models with Early Exits Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T05:53:44.511807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:53:44.511807Z digest=sha256:3fae971018ef828396f56eff08a8f41e36586901eb01b486cbd302a7227113f9

Observation a6482387-ad91-4f2c-a910-881bb09f6f81 · inbound

All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding cites this paper.

All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:31:03.955460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T15:26:55.369840Z digest=sha256:d906253ab8f8e712f9519c9118e92f5976585fe3b84fe6daa22c4124a18ca7d9