FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering | Paper Citation Record

Observation 99298249-d5ce-4d24-b7ed-7a2ec893d70d · outbound

This paper cites To- wards Causal VQA: Revealing and Reducing Spurious Cor- relations by Invariant and Covariant Semantic Editing.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering To- wards Causal VQA: Revealing and Reducing Spurious Cor- relations by Invariant and Covariant Semantic Editing

Reference 1

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Observation 7c3886df-9f77-4bec-8bf0-cc141573074c · outbound

This paper cites Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

Reference 2

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Observation 6ad4b3af-339d-4ff5-854f-99457823b23a · outbound

This paper cites Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization

Reference 3

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This paper cites PaliGemma: A versatile 3B VLM for transfer.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering PaliGemma: A versatile 3B VLM for transfer

Reference 4

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Observation 9ebf6be0-401f-4a07-97d5-2046c39be37b · outbound

This paper cites Cunningham.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Cunningham

Reference 5

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Observation 8ba14324-63cd-4c7b-a137-3ebabc3d09e7 · outbound

This paper cites VizWiz: nearly real-time answers to visual questions.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering VizWiz: nearly real-time answers to visual questions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:36.028569Z

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Observation e50859f1-9e25-4f6f-b1d8-9dbd59188da5 · outbound

This paper cites Behind the scene: Revealing the secrets of pre-trained vision-and-language models, 2020.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Behind the scene: Revealing the secrets of pre-trained vision-and-language models, 2020

Reference 7

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Observation 77e73c75-bb3d-49f4-b248-701bdd331e89 · outbound

This paper cites Benchmarking robustness of adaptation methods on pre-trained vision-language models, 2023.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Benchmarking robustness of adaptation methods on pre-trained vision-language models, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.718142Z

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Observation 44971840-6caa-4117-abe1-8330f81867b9 · outbound

This paper cites Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering

Reference 9

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Observation c0f29e56-f5ff-4e9f-b049-7c03a6c03f70 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Imagenet: A large-scale hierarchical image database

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:35.581963Z

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Observation aea9155f-6a9a-4985-9e7e-b5502aed4e13 · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding, 2019.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Bert: Pre-training of deep bidirectional trans- formers for language understanding, 2019

Reference 11

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Observation 82cba0ee-3bdf-4bd5-9d4b-0a5e2146c87a · outbound

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FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Unresolved cited work

Reference 12

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Observation 10ed4b95-718c-4c46-8997-a868a8d4c553 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 13

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Observation 3fd68671-db85-402f-9296-56412498f622 · outbound

This paper cites Ex- ploring the limits of out-of-distribution detection, 2021.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Ex- ploring the limits of out-of-distribution detection, 2021

Reference 14

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Observation 3bcc6fc5-54b4-4187-8022-96eedcfaa940 · outbound

This paper cites VQA-LOL: Visual Question Answering under the Lens of Logic.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering VQA-LOL: Visual Question Answering under the Lens of Logic

Reference 15

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Observation c8732f46-27d1-4d29-8f49-7bf25efb7c6e · outbound

This paper cites paligemma-3b-pt-224.https : / / huggingface.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering paligemma-3b-pt-224.https : / / huggingface

Reference 16

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Observation be0a20f7-6c23-4b5e-9163-7a69bc48e666 · outbound

This paper cites Distance-Based Regularisation of Deep Networks for Fine-Tuning.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Distance-Based Regularisation of Deep Networks for Fine-Tuning

Reference 17

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Observation faf0a017-4266-436e-8b13-55f39c81540f · outbound

This paper cites Finetune like you pretrain: Im- proved finetuning of zero-shot vision models.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Finetune like you pretrain: Im- proved finetuning of zero-shot vision models

Reference 18

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Observation f59dcad5-8993-44fe-b3f2-142718843a77 · outbound

This paper cites Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

Reference 19

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Observation e87c518b-01a9-425f-9299-bd734b8ffbc4 · outbound

This paper cites Rasch, Bern- hard Scholkopf, and Alexander J.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Rasch, Bern- hard Scholkopf, and Alexander J

Reference 20

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Observation c1e5c8ef-62a2-40d5-82f1-ce0e51eaea32 · outbound

This paper cites The many faces of robust- ness: A critical analysis of out-of-distribution generalization.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 21

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Observation 2634c300-530b-41dd-a524-07c7847c98ad · outbound

This paper cites Natural adversarial examples.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Natural adversarial examples

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.128657Z

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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.937969Z

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Observation 1a0fa88d-cea0-4893-9918-b7a371fa3a56 · outbound

This paper cites Llm-adapters: An adapter family for parameter- efficient fine-tuning of large language models, 2023.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Llm-adapters: An adapter family for parameter- efficient fine-tuning of large language models, 2023

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.789467Z

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Observation f819ba40-316b-4a04-af4b-b2d3997b083c · outbound

This paper cites Directional gradient pro- jection for robust fine-tuning of foundation models, 2025.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Directional gradient pro- jection for robust fine-tuning of foundation models, 2025

Reference 25

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Observation 5b9d8667-9123-4559-9f65-d7330808bd6e · outbound

This paper cites Hudson and Christopher D.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Hudson and Christopher D

Reference 26

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This paper cites Roses are red, violets are blue.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Roses are red, violets are blue

Reference 27

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Observation 38c1c61e-f2ce-4191-878a-a44fc2f8c45c · outbound

This paper cites Fine-Tuning can Distort Pre- trained Features and Underperform Out-of-Distribution,.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Fine-Tuning can Distort Pre- trained Features and Underperform Out-of-Distribution,

Reference 28

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FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Unresolved cited work

Reference 29

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Observation 1fd31f06-8625-42f2-9ae5-51f6f54e6a0d · outbound

This paper cites A Closer Look at the Robustness of Vision-and-Language Pre-trained Models,.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering A Closer Look at the Robustness of Vision-and-Language Pre-trained Models,

Reference 30

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Observation fb99404b-2d19-48aa-870b-19bb764409bc · outbound

This paper cites Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models

Reference 31

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This paper cites Explicit Inductive Bias for Transfer Learning with Convolutional Networks.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Explicit Inductive Bias for Transfer Learning with Convolutional Networks

Reference 32

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This paper cites A Closer Look at the Robustness of Vision-and-Language Pre-trained Models.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering A Closer Look at the Robustness of Vision-and-Language Pre-trained Models

Reference 33

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This paper cites Robust Visual Ques- tion Answering: Datasets, Methods, and Future Challenges,.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Robust Visual Ques- tion Answering: Datasets, Methods, and Future Challenges,

Reference 34

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Observation 4269af3a-bb07-4034-97a6-0a7463af80b3 · outbound

This paper cites Ok-vqa: A visual question answering benchmark requiring external knowledge, 2019.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Ok-vqa: A visual question answering benchmark requiring external knowledge, 2019

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This paper cites Visual instruction tuning, 2023.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Visual instruction tuning, 2023

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Observation b7231ea7-fe95-46c9-9adb-f7da0ac9cfb9 · outbound

This paper cites Maximum mean discrep- ancy for generalization in the presence of distribution and missingness shift, 2022.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Maximum mean discrep- ancy for generalization in the presence of distribution and missingness shift, 2022

Reference 37

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Observation 0e73b822-b0da-4290-9ba1-a09fb3a64d27 · outbound

This paper cites Moment matching for multi-source domain adaptation.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Moment matching for multi-source domain adaptation

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Observation 88f7ff7c-038f-4bda-b3a1-b973613dad95 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Learning Transferable Visual Models From Natural Language Supervision

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Observation 61fbbd8a-08f4-49c3-8cb9-3fdc7df2d140 · outbound

This paper cites Generalized out-of-distribution detection and be- yond in vision language model era: A survey, 2024.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Generalized out-of-distribution detection and be- yond in vision language model era: A survey, 2024

Reference 40

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This paper cites Cycle-Consistency for Robust Visual Question Answering,.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Cycle-Consistency for Robust Visual Question Answering,

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Observation d0823e45-7a42-42a4-b1a4-ca9660017ca5 · outbound

This paper cites Human-Adversarial Visual Question Answering.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Human-Adversarial Visual Question Answering

Reference 42

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Observation fd9ff035-e16b-4adf-9f57-3b6b0094dd7f · outbound

This paper cites Benchmarking out-of- distribution detection in visual question answering, 2024.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Benchmarking out-of- distribution detection in visual question answering, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.214236Z

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Observation 2388f9b5-1477-41d1-8439-7a3747386f0b · outbound

This paper cites Do imagenet classifiers generalize to im- agenet? InInternational Conference on Machine Learning, pages 5389–5400.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Do imagenet classifiers generalize to im- agenet? InInternational Conference on Machine Learning, pages 5389–5400

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.584883Z

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Observation 8bbb6535-3567-4ef4-aa60-0102cf1f9303 · outbound

This paper cites Towards VQA Models That Can Read.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Towards VQA Models That Can Read

Reference 45

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Observation 0e42019f-cbda-4e04-ba92-79ac1ec40df9 · outbound

This paper cites Trainable Projected Gradient Method for Robust Fine-tuning.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Trainable Projected Gradient Method for Robust Fine-tuning

Reference 46

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Observation 33668d0f-2c8c-4cc1-ac66-6d6060229712 · outbound

This paper cites Fast Trainable Projection for Robust Fine-Tuning,.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Fast Trainable Projection for Robust Fine-Tuning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.993713Z

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Observation 47b5d286-8974-4be9-89b1-1f5097860ca0 · outbound

This paper cites Rethinking weight decay for robust fine-tuning of foundation models,.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Rethinking weight decay for robust fine-tuning of foundation models,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.903064Z

Source-reported events for the cited work

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Observation 811af034-14bf-4280-b2cd-fe90acc791f7 · outbound

This paper cites Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA

Reference 49

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Observation 1904a24e-0b23-4a90-b896-316e078bdc05 · outbound

This paper cites Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Learning robust global representations by penalizing local predictive power.Advances in Neural Information Pro- cessing Systems, 32, 2019

Reference 50

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Source-reported events for the cited work

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Observation 384808c0-cd99-425e-bbff-20e5b2a95988 · outbound

This paper cites Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Si- mon Kohl, Taylan Cemgil, S.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Si- mon Kohl, Taylan Cemgil, S

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.671410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d9224da-b56a-4bd2-9780-c3c15738bbe4 · outbound

This paper cites Robust fine-tuning of zero-shot models.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Robust fine-tuning of zero-shot models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:24.804961Z

Source-reported events for the cited work

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Observation 4c6aae56-da85-417d-b12f-b6f063fb4c4e · outbound

This paper cites Domain-robust vqa with di- verse datasets and methods but no target labels, 2021.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Domain-robust vqa with di- verse datasets and methods but no target labels, 2021

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.489788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 941f0fc7-0f4d-4992-9b64-cae1526f5f1b · outbound

This paper cites Minigpt-4: Enhancing vision-language understanding with advanced large language models, 2023.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Minigpt-4: Enhancing vision-language understanding with advanced large language models, 2023

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.323868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2431941-ff05-4f0e-b6d8-23218ac4ef36 · outbound

This paper cites VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:30:26.964752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80a62b49-695d-4d90-ba59-05d7e6b9cb98 · outbound

This paper cites We use the LA VIS [29] public repository to fine-tune all methods.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering We use the LA VIS [29] public repository to fine-tune all methods

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.195941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69fa2271-0d8d-48be-93be-d702805d9b76 · outbound

This paper cites an unresolved cited work.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:30:29.997695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fb31c073-9b8d-4691-bbd2-38ea04c2bb51 · outbound

This paper cites 7 shows the correlation between shift and performance for different embeddings under different fine-tuning meth- ods.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering 7 shows the correlation between shift and performance for different embeddings under different fine-tuning meth- ods

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.837989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be9795bd-9761-486a-a85a-21852dd5a165 · outbound

This paper cites 5 shows the heatmap of the correlation between uni- modal and multi-modal shifts per dataset.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering 5 shows the heatmap of the correlation between uni- modal and multi-modal shifts per dataset

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.630166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7f031ed-deeb-45c6-932c-10e582a2fabb · outbound

This paper cites 13 and 14 show the variation of MIv and MIq w.r.t.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering 13 and 14 show the variation of MIv and MIq w.r.t

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.448148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84e2c426-a2b8-4bfa-bdec-637609601f3e · outbound

This paper cites 8, including LLaV A- 7B [33] with LoRA and PaliGemma-3B with full fine- tuning.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering 8, including LLaV A- 7B [33] with LoRA and PaliGemma-3B with full fine- tuning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.274521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1692ec5f-f3e9-49f8-a3bb-aef9de954d75 · outbound

This paper cites The only exception, GQA-OOD [27] (based on GQA [26]), has only answer shifts.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering The only exception, GQA-OOD [27] (based on GQA [26]), has only answer shifts

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.064764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21b24e27-5652-4d7f-a8f0-b4f6b21c4e7e · outbound

This paper cites We further compare shifts using Maximum Mean Discrepancy (MMD) [12, 20, 37] with RBF kernel in Tab.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering We further compare shifts using Maximum Mean Discrepancy (MMD) [12, 20, 37] with RBF kernel in Tab

Reference 68

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T13:30:28.908369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58e707d3-ed14-4556-88d1-c455496b3fa2 · outbound

This paper cites This also serves as a veri- fication of the reliability in quantifying shifts via feature- based representations.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering This also serves as a veri- fication of the reliability in quantifying shifts via feature- based representations

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.802066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 975ea182-8c39-49c2-a8b6-9a6b49c69ca0 · outbound

This paper cites Cycle-Consistency for Robust Visual Question Answering.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Cycle-Consistency for Robust Visual Question Answering

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:23.772858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79e8ca76-f1a5-4db2-b092-1033396ce1e4 · outbound

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

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:21.655216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e257422a-c06e-47bf-8cc4-76b650191457 · outbound

This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:22.333864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d014ed24-983a-45e9-8e21-1acf215453ba · outbound

This paper cites Fast Trainable Projection for Robust Fine-Tuning.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Fast Trainable Projection for Robust Fine-Tuning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:24.269594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46c7e70c-9d2e-4cca-9013-9010f64a7775 · outbound

This paper cites Robust Visual Question Answering: Datasets, Methods, and Future Challenges.

FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering Robust Visual Question Answering: Datasets, Methods, and Future Challenges

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:23.075341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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