Paper Citation Record · LEDGER

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis

As of 14 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 2 inbound Pith citation observations for arXiv:2412.03665.

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

pith.paper-citation-record.v1
2412.03665 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:16:58.754447Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:46:34.401485Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:45:52.393487Z

Reference resolution

70 of 70 outbound references displayed

verified exact0
verified fuzzy43
unresolved27
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88aff19c-47dd-4379-b481-3392b6cf10ce · outbound

This paper cites GPT-4 Technical Report.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis GPT-4 Technical Report

Reference 1

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Observation 0f5c1fa7-5043-4211-b46d-1a118c9f3d00 · outbound

This paper cites In: ICCV (2019) Personalizing Multimodal Large Language Models for Image Captioning 15.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICCV (2019) Personalizing Multimodal Large Language Models for Image Captioning 15

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:59.237009Z

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Observation 910e4384-33dd-4150-8624-aacdd8ee9700 · outbound

This paper cites In: NeurIPS (2022).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: NeurIPS (2022)

Reference 3

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Source-reported events for the cited work

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Observation cd854f97-112a-43e4-bc44-a205ad45c790 · outbound

This paper cites In: ECCV (2016).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ECCV (2016)

Reference 4

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Observation 3764e77d-439e-4868-a124-38ade9500983 · outbound

This paper cites In: CVPR (2018).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2018)

Reference 5

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Observation e4f7a5e1-d867-4d2d-8453-68ea91ec47f0 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis Gemini: A Family of Highly Capable Multimodal Models

Reference 6

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Source-reported events for the cited work

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Observation 995aa4ff-f4a5-4a0e-9190-2ec5bfeba01b · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T22:16:58.592925Z

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Observation 3e3cabb5-00f7-43b4-a5e7-175db1906366 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 8

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Observation 6ebf49cd-7a18-4a18-8b92-a1d61af78b4b · outbound

This paper cites In: ACL Workshops (2005).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ACL Workshops (2005)

Reference 9

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Observation 5754b34e-a594-4334-81a8-3065d9f07345 · outbound

This paper cites In: CVPR Workshops (2022).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR Workshops (2022)

Reference 10

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Observation 86e52088-6dd4-419b-aeb1-4ab6bba63b3e · outbound

This paper cites In: ICCV (2023).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICCV (2023)

Reference 11

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Observation cc4d6944-e606-463d-a5e6-d9b7746791d3 · outbound

This paper cites In: NeurIPS (2020).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: NeurIPS (2020)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:59.190462Z

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Observation 35e36477-59b0-4780-b077-34a7af105be3 · outbound

This paper cites In: ACL Findings (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ACL Findings (2024)

Reference 13

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Observation 2138525e-80f8-4c82-8cb0-8042c9a11ded · outbound

This paper cites In: CVPR Workshops (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR Workshops (2024)

Reference 14

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Observation 8a1b7b9c-7d17-479a-af75-df22f09efc9a · outbound

This paper cites In: CVPR (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2024)

Reference 15

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Observation a2bf7dea-865d-4ee1-9603-7fe25d30cfbc · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 16

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Observation 2151e4eb-b94b-46c7-befc-d70bd9392107 · outbound

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Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis Unresolved cited work

Reference 17

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Observation 88470436-b342-4621-82b2-bf6a7ebf575a · outbound

This paper cites In: NAACL (2022).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: NAACL (2022)

Reference 18

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Observation d2582b90-0d09-4e29-86c2-5f976391ca3f · outbound

This paper cites In: ICRA (2020).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICRA (2020)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:59.150266Z

Source-reported events for the cited work

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Observation 9abdeb89-358e-4b48-9f75-bd6887b71352 · outbound

This paper cites AI Communications35(2), 111–129 (2022) 16 D.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis AI Communications35(2), 111–129 (2022) 16 D

Reference 20

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Observation 7ed477d6-b019-43b3-8bfb-aa39aab00757 · outbound

This paper cites In: CVPR (2023).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2023)

Reference 21

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Observation e79dbcf7-d40c-47c4-b121-b135fe6c5c1b · outbound

This paper cites In: NeurIPS (2023).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: NeurIPS (2023)

Reference 22

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Observation 27d51029-679a-4be9-8767-3a5fe3267e12 · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 23

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Observation 0ea61bf5-f009-4a01-9953-6a6494dbdd55 · outbound

This paper cites In: ECCV (2020).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ECCV (2020)

Reference 24

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Observation 9238119e-4856-41f7-b97e-9c28fbcadbc1 · outbound

This paper cites WARP: Word-level Adversarial ReProgramming.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis WARP: Word-level Adversarial ReProgramming

Reference 25

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Observation a53b8984-6834-444b-a697-644b86da5462 · outbound

This paper cites In: EMNLP (2021).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: EMNLP (2021)

Reference 26

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Observation 7edc9b36-77b7-4f53-b0ea-dd5389aafefe · outbound

This paper cites In: ICLR (2021).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICLR (2021)

Reference 27

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Observation ebe83922-685f-43e9-98bc-44c951c2eea6 · outbound

This paper cites In: ICCV (2019).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICCV (2019)

Reference 28

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Observation 206f6240-2aad-4a67-924f-f7c60e6fc1fb · outbound

This paper cites In: ICML (2021).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICML (2021)

Reference 29

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Observation c9d05278-9a27-4641-9021-f5f92fc830f6 · outbound

This paper cites In: CVPR (2015).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2015)

Reference 30

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This paper cites In: ICCV (2023).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICCV (2023)

Reference 31

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Observation 391ecc13-847a-4d78-9b7d-b3fd59875024 · outbound

This paper cites IJCV128(7), 1956–1981 (2020).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis IJCV128(7), 1956–1981 (2020)

Reference 32

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Observation aee69a7a-4216-4aec-871d-b8fb996824a2 · outbound

This paper cites In: EMNLP (2021).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: EMNLP (2021)

Reference 33

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Observation d6592922-bfbe-4c72-a979-c1baa20be5ce · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 34

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

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 35

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Observation a019a3fd-76a0-4199-95cc-cf7e4430cf55 · outbound

This paper cites In: CVPR (2022).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2022)

Reference 36

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Observation 1f6004f9-19fe-4d08-89c9-a65cabb75b48 · outbound

This paper cites In: ACL Workshops (2004).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ACL Workshops (2004)

Reference 37

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Observation 27b982ec-3669-44fb-bede-41011964ecbf · outbound

This paper cites In: ECCV (2014).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ECCV (2014)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:59.044016Z

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Observation ef243cb5-94ad-40e3-8a7b-da1589174f68 · outbound

This paper cites SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Reference 39

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This paper cites In: ICLR (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICLR (2024)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:59.036899Z

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Observation 5b8bb909-92ba-4f67-b342-362f2725b765 · outbound

This paper cites In: CVPR (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2024)

Reference 41

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Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis Unresolved cited work

Reference 42

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Observation e530ccb5-fe98-46d8-8257-28e9122ed245 · outbound

This paper cites In: NeurIPS (2023).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: NeurIPS (2023)

Reference 43

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Observation 08ef1a79-1e93-4578-988a-e78a6eefeadd · outbound

This paper cites In: ICML (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICML (2024)

Reference 44

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Observation 8af3cdca-f7f8-4231-8e3c-5050f44c865f · outbound

This paper cites AI Open (2023).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis AI Open (2023)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:59.007392Z

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Observation d23ffe5e-79a5-40e2-83da-a342bb42bef7 · outbound

This paper cites ClipCap: CLIP Prefix for Image Captioning.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis ClipCap: CLIP Prefix for Image Captioning

Reference 46

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This paper cites IEEE Intelligent Systems (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis IEEE Intelligent Systems (2024)

Reference 47

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Observation c3834755-49ea-4860-b4f3-7e9272b9d0bf · outbound

This paper cites In: BMVC (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: BMVC (2024)

Reference 48

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Observation c83fb3cb-40e8-4d9e-8c93-dc2610e2dba9 · outbound

This paper cites In: NeurIPS (2022).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: NeurIPS (2022)

Reference 49

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Observation 5c70fbae-54eb-4f02-926c-c904bf16c1be · outbound

This paper cites In: ACL (2002).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ACL (2002)

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.980571Z

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Observation a521a331-500c-417d-b8ba-dcd6158b7a0b · outbound

This paper cites In: ICML (2021).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ICML (2021)

Reference 51

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Observation f0df053e-ff07-4e7b-b4ea-aec08fd98c96 · outbound

This paper cites In: CVPR (2023).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2023)

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.968770Z

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Observation e97c0f19-db0a-4dfe-8f09-a6fa0876f523 · outbound

This paper cites In: CVPR (2017).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2017)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.960932Z

Source-reported events for the cited work

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Observation 339dc2fa-210f-4bac-b13f-610edc63035b · outbound

This paper cites In: CVPR (2023).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2023)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.953356Z

Source-reported events for the cited work

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This paper cites In: ECCV (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ECCV (2024)

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.946238Z

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 17f48872-303c-46c8-80c0-10583998400f · outbound

This paper cites In: ACL (2018).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ACL (2018)

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.938784Z

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 8da7e177-7d18-444e-a67c-0e9d320bc116 · outbound

This paper cites In: ECCV (2020).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: ECCV (2020)

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.931253Z

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 adc2fd34-5175-46c3-b673-255457601811 · outbound

This paper cites In: CVPR (2010).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2010)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.923858Z

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 187fdc54-7bf0-4347-a9eb-796a5d6aa4cd · outbound

This paper cites Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Reference 59

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This paper cites In: CVPR (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2024)

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T22:16:58.727872Z

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Observation 66ef6ac9-5a83-4d59-8b4c-de9e6c9a6a58 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis LLaMA: Open and Efficient Foundation Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T22:16:58.731179Z

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Unavailable: canonical work link unavailable.

Observation 7151421f-b469-4715-a8b2-1c2ff8e6e526 · outbound

This paper cites In: CVPR (2015).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2015)

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.911614Z

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 ad689c1a-694b-41d6-b2ef-346c4ead1263 · outbound

This paper cites In: CVPR (2015) 18 D.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2015) 18 D

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.904323Z

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 2106f69c-6cf8-451a-9f0a-b415e65e0a57 · outbound

This paper cites In: AAAI (2024).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: AAAI (2024)

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.897106Z

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 6146e7bc-49c0-40c8-8f60-38f6987a6fd9 · outbound

This paper cites CogVLM: Visual Expert for Pretrained Language Models.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis CogVLM: Visual Expert for Pretrained Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T22:16:58.741333Z

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Observation 7cd75297-620f-4325-8d9c-a94eaed1a975 · outbound

This paper cites In: CVPR (2019).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis In: CVPR (2019)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.889719Z

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 2ad190ec-6c3a-4ad2-8675-2c52b3184f45 · outbound

This paper cites Proceedings of the IEEE98(8) (2010).

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis Proceedings of the IEEE98(8) (2010)

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:16:58.881946Z

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 7c5c315c-baa8-4461-890b-879b7c2e9386 · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T22:16:58.748692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61a90ef5-6057-4d49-8292-acf4db629436 · outbound

This paper cites Woodpecker: Hallucination Correction for Multimodal Large Language Models.

Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T22:16:58.751793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 262c8a4c-f739-4d70-b9f4-bd242d2fa1d1 · outbound

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Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis SVIT: Scaling up Visual Instruction Tuning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T22:16:58.754447Z

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Pith citing papers

Observation cf8fdaf7-4fae-4a8c-bbdb-27597834ea0e · inbound

R-Genie: Reasoning-Guided Generative Image Editing cites this paper.

R-Genie: Reasoning-Guided Generative Image Editing Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis

Reference 3

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Observation 939add0e-cde7-4d97-a8f7-4d8a88594e64 · inbound

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos cites this paper.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis

Reference 6

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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.