Paper Citation Record · LEDGER

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models

As of 10 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2502.06094.

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

pith.paper-citation-record.v1
2502.06094 v1

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measured 41 of 41 reference resolution

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External citation measurements

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Outbound references

Observation cf95cc62-26e3-4412-bc85-d71b1f52c0ab · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 1

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Observation e59ff404-dcd7-484b-ad17-4c5c90dee385 · outbound

This paper cites MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale

Reference 4

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This paper cites Beyond bias and discrimination: re- defining the ai ethics principle of fairness in healthcare machine-learning algorithms.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Beyond bias and discrimination: re- defining the ai ethics principle of fairness in healthcare machine-learning algorithms

Reference 6

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Observation 13e38297-da8c-454a-98cd-cfe710599a3d · outbound

This paper cites Eclb: Efficient contrastive learn- ing on bi-level for noisy labels.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Eclb: Efficient contrastive learn- ing on bi-level for noisy labels

Reference 8

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Observation d591587a-2e55-49c7-bd32-6257362fd8d5 · outbound

This paper cites A visual–language foundation model for pathology im- age analysis using medical twitter.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models A visual–language foundation model for pathology im- age analysis using medical twitter

Reference 9

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Observation 37b9f081-da2f-4181-b09a-bd29432f5e27 · outbound

This paper cites Jacobs, Michael I.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Jacobs, Michael I

Reference 10

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Observation 59607e9e-8d0a-40a1-b89d-72ae8cbf18d5 · outbound

This paper cites VisionGPT: Vision-Language Understanding Agent Using Generalized Multimodal Framework.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models VisionGPT: Vision-Language Understanding Agent Using Generalized Multimodal Framework

Reference 12

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Observation ad7fcd4c-2f5c-48de-9efd-d9ee480d4c48 · outbound

This paper cites How fair are medical imaging foundation models? In Machine Learning for Health (ML4H) , pages 217–231.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models How fair are medical imaging foundation models? In Machine Learning for Health (ML4H) , pages 217–231

Reference 13

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Observation 5ed55075-f97a-4629-91a0-fd55ef63a4f1 · outbound

This paper cites A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis

Reference 14

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Observation 073f154f-4a3c-4477-a1a3-5ba54807f85b · outbound

This paper cites Vpl: Visual proxy learning framework for zero-shot medical image diagnosis.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Vpl: Visual proxy learning framework for zero-shot medical image diagnosis

Reference 15

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Observation 81b28f75-fea1-4ef7-b20c-9f07cd26a2c0 · outbound

This paper cites Medcot: Medical chain of thought via hierarchical expert.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Medcot: Medical chain of thought via hierarchical expert

Reference 16

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Observation 75bfb090-2f05-488c-a2e5-2a9c6989e8cc · outbound

This paper cites KPL: Training-Free Medical Knowledge Mining of Vision-Language Models.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models KPL: Training-Free Medical Knowledge Mining of Vision-Language Models

Reference 17

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This paper cites Biogpt: generative pre-trained transformer for biomedical text generation and mining.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Biogpt: generative pre-trained transformer for biomedical text generation and mining

Reference 19

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Observation c97c3e4a-95ee-44f9-947c-73b31d7964bf · outbound

This paper cites Fairclip: Harnessing fairness in vision-language learning.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Fairclip: Harnessing fairness in vision-language learning

Reference 20

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Observation a344f343-c268-4913-9b18-fb87f67564b1 · outbound

This paper cites A survey on bias and fairness in machine learning.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models A survey on bias and fairness in machine learning

Reference 21

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Observation 8289ecd6-d8a2-4c27-9d64-ed1c5029d0f4 · outbound

This paper cites Fairness in deep learning: A survey on vision and language research.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Fairness in deep learning: A survey on vision and language research

Reference 22

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This paper cites Com- putational optimal transport: With applications to data sci- ence.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Com- putational optimal transport: With applications to data sci- ence

Reference 23

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This paper cites Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study

Reference 26

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This paper cites Scaling vision with sparse mixture of experts.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Scaling vision with sparse mixture of experts

Reference 27

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Observation 9a737845-e2ca-41a0-8384-5d849553a589 · outbound

This paper cites Dr-fairness: Dynamic data ratio adjustment for fair training on real and generated data.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Dr-fairness: Dynamic data ratio adjustment for fair training on real and generated data

Reference 28

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Observation d638f87a-6220-4167-bac9-c549f4c43479 · outbound

This paper cites FEAMOE: Fair, Explainable and Adaptive Mixture of Experts.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models FEAMOE: Fair, Explainable and Adaptive Mixture of Experts

Reference 29

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Observation a2ffbeb5-6b4f-4df3-9792-1c2f26dba193 · outbound

This paper cites Conceptualising fairness: three pillars for medical algorithms and health equity.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Conceptualising fairness: three pillars for medical algorithms and health equity

Reference 30

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This paper cites Fairness-related performance and explainability effects in deep learning models for brain image analysis.Journal of Medical Imag- ing, 9(6):061102–061102,.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Fairness-related performance and explainability effects in deep learning models for brain image analysis.Journal of Medical Imag- ing, 9(6):061102–061102,

Reference 31

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Observation aecda6b9-bc1a-49ff-b037-4ac74017d64d · outbound

This paper cites FairViT: Fair Vision Transformer via Adaptive Masking.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models FairViT: Fair Vision Transformer via Adaptive Masking

Reference 32

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This paper cites Tsnet: Integrating dental position prior and symptoms for tooth segmentation from cbct images.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Tsnet: Integrating dental position prior and symptoms for tooth segmentation from cbct images

Reference 33

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This paper cites Principles of clinical ethics and their application to practice.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Principles of clinical ethics and their application to practice

Reference 34

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Observation 1b6262e1-a63e-4795-bf68-b014091e4e23 · outbound

This paper cites MedCLIP: Contrastive Learning from Unpaired Medical Images and Text.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Reference 35

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This paper cites Multidisciplinary considerations of fairness in medical ai: A scoping review.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Multidisciplinary considerations of fairness in medical ai: A scoping review

Reference 36

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This paper cites ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

Reference 37

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This paper cites Addressing fairness issues in deep learning-based medical image analysis: a systematic review.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Addressing fairness issues in deep learning-based medical image analysis: a systematic review

Reference 38

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Observation b2808f99-a5ef-47d4-909e-c33aad89621b · outbound

This paper cites Infrared and visible image fusion via texture conditional generative adversarial network.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Infrared and visible image fusion via texture conditional generative adversarial network

Reference 39

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This paper cites WorldGPT: A Sora-Inspired Video AI Agent as Rich World Models from Text and Image Inputs.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models WorldGPT: A Sora-Inspired Video AI Agent as Rich World Models from Text and Image Inputs

Reference 40

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This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 41

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This paper cites VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models VisionGPT-3D: A Generalized Multimodal Agent for Enhanced 3D Vision Understanding

Reference 1991

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This paper cites Justice: a key consideration in health policy and systems research ethics.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Justice: a key consideration in health policy and systems research ethics

Reference 2019

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This paper cites Fairness-aware Vision Transformer via Debiased Self-Attention.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Fairness-aware Vision Transformer via Debiased Self-Attention

Reference 2020

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This paper cites Switch transformers: Scaling to trillion param- eter models with simple and efficient sparsity.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Switch transformers: Scaling to trillion param- eter models with simple and efficient sparsity

Reference 2021

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Observation 28306441-c20e-4965-9d80-5ee074badffc · outbound

This paper cites E ad- dressing fairness in artificial intelligence for medical imag- ing nat.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models E ad- dressing fairness in artificial intelligence for medical imag- ing nat

Reference 2022

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This paper cites Algorithmic encoding of protected characteristics in chest x-ray disease detection models.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Algorithmic encoding of protected characteristics in chest x-ray disease detection models

Reference 2023

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Observation d4cd673b-31e7-4bb3-bce8-9921a53343df · outbound

This paper cites Fairmoe: counterfactually-fair mixture of experts with levels of interpretability.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Fairmoe: counterfactually-fair mixture of experts with levels of interpretability

Reference 2024

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Observation 5749d065-0f95-430e-a5b0-1835c42de703 · outbound

This paper cites Cross-token Modeling with Conditional Computation.

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models Cross-token Modeling with Conditional Computation

Reference 2025

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