Paper Citation Record · LEDGER

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2412.03927.

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

pith.paper-citation-record.v1
2412.03927 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:01:53.257864Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:51:02.054133Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T11:13:02.866955Z

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e73f74d0-b785-40ba-9b88-4c9db2fe5aa5 · outbound

This paper cites GPT-4 Technical Report.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

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Observation 9cf6613d-11c8-4c85-b91a-854f9726d22f · outbound

This paper cites Vqa: Visual question answering.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Vqa: Visual question answering

Reference 2

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Source-reported events for the cited work

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Observation 425ab362-8b3f-4960-bd79-60b8621fe0ab · outbound

This paper cites Invariant Risk Minimization.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Invariant Risk Minimization

Reference 3

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Observation 4dbf04fe-2eba-468b-b868-4f30e9b057cc · outbound

This paper cites The iWildCam 2020 Competition Dataset.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models The iWildCam 2020 Competition Dataset

Reference 4

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Observation c94df8e9-9368-450a-9805-e27fd0b60a94 · outbound

This paper cites ColorSense: A Study on Color Vision in Machine Visual Recognition.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models ColorSense: A Study on Color Vision in Machine Visual Recognition

Reference 5

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Observation 9e7e28a8-3540-4a27-bf53-65cdd5ca8722 · outbound

This paper cites Better may not be fairer: A study on subgroup discrepancy in image classification.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Better may not be fairer: A study on subgroup discrepancy in image classification

Reference 6

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Observation 23fd4988-4699-4589-acaf-6a53ba76602e · outbound

This paper cites Probable domain generalization via quantile risk minimization.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Probable domain generalization via quantile risk minimization

Reference 7

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Observation 343aed6e-8af6-44cf-9774-6d486f7f75b1 · outbound

This paper cites Mme: A compre- hensive evaluation benchmark for multimodal large language models, 2024.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Mme: A compre- hensive evaluation benchmark for multimodal large language models, 2024

Reference 8

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Observation e3633eb1-1723-4db4-b386-0c0ec4d54c22 · outbound

This paper cites Car colour and risk of car crash injury: population based case control study.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Car colour and risk of car crash injury: population based case control study

Reference 9

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Observation b25ecf3a-e6ae-43df-899b-f2ce028d9e45 · outbound

This paper cites In search of lost do- main generalization, 2020.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models In search of lost do- main generalization, 2020

Reference 10

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Observation 4a2795e1-08c3-4929-849b-af1536e38e24 · outbound

This paper cites Efficient Multimodal Learning from Data-centric Perspective.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Efficient Multimodal Learning from Data-centric Perspective

Reference 11

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Observation 9de9b261-60b8-46c9-893a-4299fe1e22c2 · outbound

This paper cites Benchmarking neu- ral network robustness to common corruptions and perturba- tions.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Benchmarking neu- ral network robustness to common corruptions and perturba- tions

Reference 12

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Observation 50f51838-2e43-40c8-b558-9492ce8bfb59 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

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Observation 89cca13f-e2d0-4a11-8c40-163406ac45bb · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 14

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Observation a975d886-6e08-4c1b-8814-8457658c35ca · outbound

This paper cites Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 15

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Observation fb63d161-f474-44ac-9c03-11ee594301fe · outbound

This paper cites A diagram is worth a dozen images.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models A diagram is worth a dozen images

Reference 16

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Observation 2cafec16-8eac-47a9-abad-acfd88023021 · outbound

This paper cites Selfreg: Self-supervised contrastive regu- larization for domain generalization.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Selfreg: Self-supervised contrastive regu- larization for domain generalization

Reference 17

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Observation 56eae539-b899-4f6c-b3cf-dbf619ecd21c · outbound

This paper cites Out-of-distribution general- ization via risk extrapolation (rex).

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Out-of-distribution general- ization via risk extrapolation (rex)

Reference 18

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Observation df19e9af-261b-47a5-b9ab-ef73510828f0 · outbound

This paper cites Sharegpt-4o, 2024.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Sharegpt-4o, 2024

Reference 19

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Observation d6d50cb2-3665-4ec0-aeb0-2e14c0cacfb3 · outbound

This paper cites Domain generalization with adversarial feature learning.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Domain generalization with adversarial feature learning

Reference 20

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Observation 178708c8-5280-4dfd-b2b1-9b6360e4575b · outbound

This paper cites MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts

Reference 21

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Observation 8494f5e3-4eff-4a54-a0ec-b3c16bfcffdb · outbound

This paper cites Microsoft coco: Common objects in context.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Microsoft coco: Common objects in context

Reference 22

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Observation 6c71ab8f-b354-4da8-b9f6-80117dc1975f · outbound

This paper cites Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Reference 23

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Observation dfee1dae-bfe6-4991-bcf0-689c415c5f87 · outbound

This paper cites MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

Reference 24

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Visual instruction tuning, 2023

Reference 25

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Improved baselines with visual instruction tuning

Reference 26

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This paper cites Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233

Reference 27

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Observation 150535f9-8a2e-490a-b1dc-3705e571c0ab · outbound

This paper cites Deep learning face attributes in the wild.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Deep learning face attributes in the wild

Reference 28

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This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 29

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This paper cites Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts, 2024.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Mathvista: Evaluating mathemat- ical reasoning of foundation models in visual contexts, 2024

Reference 30

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This paper cites Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases

Reference 31

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Ocr-vqa: Visual question answering by reading text in images

Reference 32

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This paper cites Understanding Domain Generalization: A Noise Robustness Perspective.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Understanding Domain Generalization: A Noise Robustness Perspective

Reference 33

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This paper cites Learning transferable visual models from natural language supervision, 2021.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Learning transferable visual models from natural language supervision, 2021

Reference 34

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This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 35

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Towards vqa models that can read

Reference 36

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Deep coral: Correlation alignment for deep domain adaptation

Reference 37

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Observation e163cef9-b5ff-4688-a293-d848d5f4f5c4 · outbound

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Principles of risk minimization for learn- ing theory

Reference 38

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Observation ce8af3ae-bde9-4d8b-a390-061a378749e2 · outbound

This paper cites Change is hard: A closer look at subpopulation shift, 2023.

MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Change is hard: A closer look at subpopulation shift, 2023

Reference 39

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 40

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models Nico++: Towards better benchmarking for domain generalization

Reference 41

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MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models 13, 11 and 12

Reference 42

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Pith citing papers

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AIDE: Agentically Improve Visual Language Model with Domain Experts cites this paper.

AIDE: Agentically Improve Visual Language Model with Domain Experts MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models

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AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models cites this paper.

AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models

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