Understanding Museum Exhibits using Vision-Language Reasoning | Paper Citation Record

Observation 7ab34dd0-f126-446a-9a2e-6666b0b96ed3 · outbound

This paper cites Artemis: Affective language for visual art.

Understanding Museum Exhibits using Vision-Language Reasoning Artemis: Affective language for visual art

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This paper cites Feelingblue: A corpus for understanding the emotional con- notation of color in context.Transactions of the Association for Computational Linguistics, 11:176–190, 2023.

Understanding Museum Exhibits using Vision-Language Reasoning Feelingblue: A corpus for understanding the emotional con- notation of color in context.Transactions of the Association for Computational Linguistics, 11:176–190, 2023

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This paper cites Vqa: Visual question answering.

Understanding Museum Exhibits using Vision-Language Reasoning Vqa: Visual question answering

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This paper cites Explain me the painting: Multi-topic knowledgeable art description gen- eration.

Understanding Museum Exhibits using Vision-Language Reasoning Explain me the painting: Multi-topic knowledgeable art description gen- eration

Reference 4

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Observation 19fa2347-3eef-48e7-a2c2-1f85b6db6be8 · outbound

This paper cites Taming CLIP for Fine-grained and Structured Visual Understanding of Museum Exhibits.

Understanding Museum Exhibits using Vision-Language Reasoning Taming CLIP for Fine-grained and Structured Visual Understanding of Museum Exhibits

Reference 5

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This paper cites Bridg- ing the gap between object and image-level representations for open-vocabulary detection.Advances in Neural Informa- tion Processing Systems, 35:33781–33794, 2022.

Understanding Museum Exhibits using Vision-Language Reasoning Bridg- ing the gap between object and image-level representations for open-vocabulary detection.Advances in Neural Informa- tion Processing Systems, 35:33781–33794, 2022

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This paper cites Clip retrieval: Easily compute clip embeddings and build a clip retrieval system with them.https : / / github.

Understanding Museum Exhibits using Vision-Language Reasoning Clip retrieval: Easily compute clip embeddings and build a clip retrieval system with them.https : / / github

Reference 7

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This paper cites Viscounth: A large-scale multilin- gual visual question answering dataset for cultural heritage.

Understanding Museum Exhibits using Vision-Language Reasoning Viscounth: A large-scale multilin- gual visual question answering dataset for cultural heritage

Reference 8

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This paper cites Predicting image aesthetics with deep learning.

Understanding Museum Exhibits using Vision-Language Reasoning Predicting image aesthetics with deep learning

Reference 9

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

Understanding Museum Exhibits using Vision-Language Reasoning Vizwiz: nearly real-time answers to visual questions

Reference 10

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This paper cites Visual question answering for cul- tural heritage.

Understanding Museum Exhibits using Vision-Language Reasoning Visual question answering for cul- tural heritage

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This paper cites Fine-tuning convolutional neural networks for fine art classification.Ex- pert Systems with Applications, 114:107–118, 2018.

Understanding Museum Exhibits using Vision-Language Reasoning Fine-tuning convolutional neural networks for fine art classification.Ex- pert Systems with Applications, 114:107–118, 2018

Reference 12

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This paper cites Uniter: Universal image-text representation learning.

Understanding Museum Exhibits using Vision-Language Reasoning Uniter: Universal image-text representation learning

Reference 13

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This paper cites Clip-art: Contrastive pre-training for fine-grained art classification.

Understanding Museum Exhibits using Vision-Language Reasoning Clip-art: Contrastive pre-training for fine-grained art classification

Reference 14

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This paper cites Learning sample difficulty from pre-trained models for reliable prediction.Advances in Neural Information Process- ing Systems, 36, 2024.

Understanding Museum Exhibits using Vision-Language Reasoning Learning sample difficulty from pre-trained models for reliable prediction.Advances in Neural Information Process- ing Systems, 36, 2024

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This paper cites Novel datasets for fine-grained image categoriza- tion.

Understanding Museum Exhibits using Vision-Language Reasoning Novel datasets for fine-grained image categoriza- tion

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This paper cites Noisyart: A dataset for webly-supervised art- work recognition.

Understanding Museum Exhibits using Vision-Language Reasoning Noisyart: A dataset for webly-supervised art- work recognition

Reference 17

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This paper cites Webly-supervised zero-shot learning for artwork instance recognition.Pattern Recognition Letters, 128:420– 426, 2019.

Understanding Museum Exhibits using Vision-Language Reasoning Webly-supervised zero-shot learning for artwork instance recognition.Pattern Recognition Letters, 128:420– 426, 2019

Reference 18

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

Understanding Museum Exhibits using Vision-Language Reasoning Imagenet: A large-scale hierarchical image database

Reference 19

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This paper cites Stytr2: Im- age style transfer with transformers.

Understanding Museum Exhibits using Vision-Language Reasoning Stytr2: Im- age style transfer with transformers

Reference 20

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This paper cites De- coupling zero-shot semantic segmentation.

Understanding Museum Exhibits using Vision-Language Reasoning De- coupling zero-shot semantic segmentation

Reference 21

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This paper cites A survey on bias in visual datasets.Computer Vision and Image Understanding, 223: 103552, 2022.

Understanding Museum Exhibits using Vision-Language Reasoning A survey on bias in visual datasets.Computer Vision and Image Understanding, 223: 103552, 2022

Reference 22

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This paper cites Are you talking to a machine? dataset and methods for multilingual image question.Advances in neural information processing systems, 28, 2015.

Understanding Museum Exhibits using Vision-Language Reasoning Are you talking to a machine? dataset and methods for multilingual image question.Advances in neural information processing systems, 28, 2015

Reference 23

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This paper cites How to read paintings: semantic art understanding with multi-modal retrieval.

Understanding Museum Exhibits using Vision-Language Reasoning How to read paintings: semantic art understanding with multi-modal retrieval

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This paper cites Knowit vqa: Answering knowledge-based ques- tions about videos.

Understanding Museum Exhibits using Vision-Language Reasoning Knowit vqa: Answering knowledge-based ques- tions about videos

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This paper cites A dataset and baselines for visual question answering on art.

Understanding Museum Exhibits using Vision-Language Reasoning A dataset and baselines for visual question answering on art

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This paper cites A dataset and baselines for visual question answering on art.

Understanding Museum Exhibits using Vision-Language Reasoning A dataset and baselines for visual question answering on art

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This paper cites Im- age style transfer using convolutional neural networks.

Understanding Museum Exhibits using Vision-Language Reasoning Im- age style transfer using convolutional neural networks

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This paper cites Aes- thetic image captioning from weakly-labelled photographs.

Understanding Museum Exhibits using Vision-Language Reasoning Aes- thetic image captioning from weakly-labelled photographs

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This paper cites Beyond language bias: Over- coming multimodal shortcut and distribution biases for ro- bust visual question answering.

Understanding Museum Exhibits using Vision-Language Reasoning Beyond language bias: Over- coming multimodal shortcut and distribution biases for ro- bust visual question answering

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This paper cites Open-vocabulary Object Detection via Vision and Language Knowledge Distillation.

Understanding Museum Exhibits using Vision-Language Reasoning Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

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Understanding Museum Exhibits using Vision-Language Reasoning Many- modalqa: Modality disambiguation and qa over diverse in- puts

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Understanding Museum Exhibits using Vision-Language Reasoning Scaling up visual and vision-language representa- tion learning with noisy text supervision

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Understanding Museum Exhibits using Vision-Language Reasoning Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

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Understanding Museum Exhibits using Vision-Language Reasoning Prompting visual-language models for efficient video understanding

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Understanding Museum Exhibits using Vision-Language Reasoning FigureQA: An Annotated Figure Dataset for Visual Reasoning

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Understanding Museum Exhibits using Vision-Language Reasoning Mdetr- modulated detection for end-to-end multi-modal understand- ing

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Understanding Museum Exhibits using Vision-Language Reasoning From word embeddings to document distances

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Understanding Museum Exhibits using Vision-Language Reasoning Clipstyler: Image style transfer with a single text condition

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Understanding Museum Exhibits using Vision-Language Reasoning Language-driven semantic seg- mentation

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Understanding Museum Exhibits using Vision-Language Reasoning Language-driven Semantic Segmentation

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Understanding Museum Exhibits using Vision-Language Reasoning Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

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Understanding Museum Exhibits using Vision-Language Reasoning VisualBERT: A Simple and Performant Baseline for Vision and Language

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Understanding Museum Exhibits using Vision-Language Reasoning Oscar: Object-semantics aligned pre-training for vision-language tasks

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Understanding Museum Exhibits using Vision-Language Reasoning Microsoft coco: Common objects in context

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Understanding Museum Exhibits using Vision-Language Reasoning Fine-grained late-interaction multi-modal retrieval for retrieval augmented visual question answering

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Understanding Museum Exhibits using Vision-Language Reasoning Visual instruction tuning, 2023

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Understanding Museum Exhibits using Vision-Language Reasoning Decoupled Weight Decay Regularization

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Understanding Museum Exhibits using Vision-Language Reasoning Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.Advances in neural information processing systems, 32, 2019

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Understanding Museum Exhibits using Vision-Language Reasoning Data- efficient image captioning of fine art paintings via virtual- real semantic alignment training.Neurocomputing, 490:163– 180, 2022

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Understanding Museum Exhibits using Vision-Language Reasoning Class-agnostic object detection with multi- modal transformer

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Understanding Museum Exhibits using Vision-Language Reasoning Fine-Grained Visual Classification of Aircraft

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Understanding Museum Exhibits using Vision-Language Reasoning A multi-world ap- proach to question answering about real-world scenes based on uncertain input.Advances in neural information process- ing systems, 27, 2014

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Understanding Museum Exhibits using Vision-Language Reasoning Ask your neurons: A neural-based approach to answering questions about images

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Understanding Museum Exhibits using Vision-Language Reasoning Ok-vqa: A visual question answering benchmark requiring external knowledge

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Observation eb64fad0-9d73-4b02-86f3-debbafd07243 · outbound

This paper cites Taylor & Francis, 2008.

Understanding Museum Exhibits using Vision-Language Reasoning Taylor & Francis, 2008

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:42.425263Z

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Observation a19689f0-cd54-4a7e-abc3-667faf400d9a · outbound

This paper cites Foundation Model is Efficient Multimodal Multitask Model Selector.

Understanding Museum Exhibits using Vision-Language Reasoning Foundation Model is Efficient Multimodal Multitask Model Selector

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Resolution: verified exact; local_arxiv, observed 2026-08-12T04:28:40.216299Z

Source-reported events for the cited work

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Observation f9aafe1e-cd7c-4ad7-b6d2-8e9df7233284 · outbound

This paper cites The rijksmuseum challenge: Museum-centered visual recognition.

Understanding Museum Exhibits using Vision-Language Reasoning The rijksmuseum challenge: Museum-centered visual recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:42.318571Z

Source-reported events for the cited work

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Observation b784edc1-0dd0-4ff1-a425-551c1f242782 · outbound

This paper cites Encyclopedic vqa: Visual questions about detailed properties of fine-grained categories.

Understanding Museum Exhibits using Vision-Language Reasoning Encyclopedic vqa: Visual questions about detailed properties of fine-grained categories

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Observation 26c6844f-aa70-497a-9df4-85d53dd1cb30 · outbound

This paper cites A dataset and a con- volutional model for iconography classification in paintings.

Understanding Museum Exhibits using Vision-Language Reasoning A dataset and a con- volutional model for iconography classification in paintings

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:42.285300Z

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Observation 51c3790b-0b65-4663-b441-bc3ac2f5e2e5 · outbound

This paper cites Expanding language-image pretrained models for gen- eral video recognition.

Understanding Museum Exhibits using Vision-Language Reasoning Expanding language-image pretrained models for gen- eral video recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:42.267591Z

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Observation 8b3dc484-7cf8-4189-9d71-02bb7a0dd433 · outbound

This paper cites A survey of geospatial semantic web for cultural heritage.

Understanding Museum Exhibits using Vision-Language Reasoning A survey of geospatial semantic web for cultural heritage

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Source-reported events for the cited work

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Observation 7482a97c-7bc9-4211-b6ac-3a571966d964 · outbound

This paper cites Suppressing biased samples for robust vqa.IEEE Transactions on Multimedia, 24:3405– 3415, 2021.

Understanding Museum Exhibits using Vision-Language Reasoning Suppressing biased samples for robust vqa.IEEE Transactions on Multimedia, 24:3405– 3415, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:41.957607Z

Source-reported events for the cited work

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Observation dc5d31ef-e9c6-4d4b-9511-18ae44cab577 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation.

Understanding Museum Exhibits using Vision-Language Reasoning Bleu: a method for automatic evaluation of machine translation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:41.941560Z

Source-reported events for the cited work

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Observation b795c7f2-ee04-4d8e-a045-5abfd8ee07a3 · outbound

This paper cites Combined scal- ing for zero-shot transfer learning.Neurocomputing, 555: 126658, 2023.

Understanding Museum Exhibits using Vision-Language Reasoning Combined scal- ing for zero-shot transfer learning.Neurocomputing, 555: 126658, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:41.925823Z

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Observation b3ce181e-f90d-4a03-993e-6941ebef5c9b · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Understanding Museum Exhibits using Vision-Language Reasoning Learning transferable visual models from natural language supervi- sion

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Resolution: unresolved; no resolver link, observed 2026-08-12T04:28:39.324548Z

Source-reported events for the cited work

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Observation 3393842a-f175-4673-bf18-b1edf5d1071e · outbound

This paper cites Zero-shot text-to-image generation.

Understanding Museum Exhibits using Vision-Language Reasoning Zero-shot text-to-image generation

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Resolution: unresolved; no resolver link, observed 2026-08-12T04:28:39.330273Z

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Observation 74699bd3-f761-4f52-9766-8131ff3b5a6e · outbound

This paper cites Fine-tuned clip models are efficient video learners.

Understanding Museum Exhibits using Vision-Language Reasoning Fine-tuned clip models are efficient video learners

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:41.730928Z

Source-reported events for the cited work

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Observation dee15f97-4987-4f51-a781-fd26ff89b7d8 · outbound

This paper cites Stylebabel: Artistic style tag- ging and captioning.

Understanding Museum Exhibits using Vision-Language Reasoning Stylebabel: Artistic style tag- ging and captioning

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Observation c979b1ca-624e-4080-8416-cca8ccfa4758 · outbound

This paper cites Viske: Visual knowledge extraction and question answering by visual verification of relation phrases.

Understanding Museum Exhibits using Vision-Language Reasoning Viske: Visual knowledge extraction and question answering by visual verification of relation phrases

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Observation c7124083-46a9-4827-bd99-77b995396922 · outbound

This paper cites A dataset for multimodal question answering in the cultural heritage domain.

Understanding Museum Exhibits using Vision-Language Reasoning A dataset for multimodal question answering in the cultural heritage domain

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Observation e259545d-07a7-4e8d-b1c1-de2dc9a6d980 · outbound

This paper cites Towards vqa models that can read.

Understanding Museum Exhibits using Vision-Language Reasoning Towards vqa models that can read

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Resolution: unresolved; no resolver link, observed 2026-08-12T04:28:39.351442Z

Source-reported events for the cited work

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Observation d8483041-520a-4f3e-835b-c6508cc17e72 · outbound

This paper cites Bioclip: A vision foundation model for the tree of life.

Understanding Museum Exhibits using Vision-Language Reasoning Bioclip: A vision foundation model for the tree of life

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Source-reported events for the cited work

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Observation bba0832d-4e52-4df3-b4e9-8017004c2042 · outbound

This paper cites Omniart: a large- scale artistic benchmark.ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 14 (4):1–21, 2018.

Understanding Museum Exhibits using Vision-Language Reasoning Omniart: a large- scale artistic benchmark.ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 14 (4):1–21, 2018

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Observation a9614f6d-52ae-4f24-bfa0-b516f6e0cca1 · outbound

This paper cites MultiModalQA: Complex Question Answering over Text, Tables and Images.

Understanding Museum Exhibits using Vision-Language Reasoning MultiModalQA: Complex Question Answering over Text, Tables and Images

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Source-reported events for the cited work

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Observation c06e7cdc-e446-442e-b6e8-a092dee5fe18 · outbound

This paper cites Ceci n’est pas une pipe: A deep convo- lutional network for fine-art paintings classification.

Understanding Museum Exhibits using Vision-Language Reasoning Ceci n’est pas une pipe: A deep convo- lutional network for fine-art paintings classification

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Observation cd9533cc-20c9-4381-b957-369923c57141 · outbound

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

Understanding Museum Exhibits using Vision-Language Reasoning Gemini: A Family of Highly Capable Multimodal Models

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Source-reported events for the cited work

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Observation f73830ac-b551-4adf-b5fa-53ed64e0ebd8 · outbound

This paper cites The caltech-ucsd birds-200–2011 dataset.

Understanding Museum Exhibits using Vision-Language Reasoning The caltech-ucsd birds-200–2011 dataset

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Source-reported events for the cited work

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Observation 9dbba161-2b99-46c5-a7e2-96ae1cca3792 · outbound

This paper cites ActionCLIP: A New Paradigm for Video Action Recognition.

Understanding Museum Exhibits using Vision-Language Reasoning ActionCLIP: A New Paradigm for Video Action Recognition

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3abc7c45-00ef-4af2-a55a-71ade113b9d1 · outbound

This paper cites Explicit Knowledge-based Reasoning for Visual Question Answering.

Understanding Museum Exhibits using Vision-Language Reasoning Explicit Knowledge-based Reasoning for Visual Question Answering

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Source-reported events for the cited work

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Observation d5deeac1-c870-458c-aa33-2d7f6d327429 · outbound

This paper cites Fvqa: Fact-based visual question an- swering.IEEE transactions on pattern analysis and machine intelligence, 40(10):2413–2427, 2017.

Understanding Museum Exhibits using Vision-Language Reasoning Fvqa: Fact-based visual question an- swering.IEEE transactions on pattern analysis and machine intelligence, 40(10):2413–2427, 2017

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Observation d8ad7152-db21-408c-8a55-867df9072f73 · outbound

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

Understanding Museum Exhibits using Vision-Language Reasoning MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

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Observation bde93852-87da-444a-9af4-350c3fbebaab · outbound

This paper cites Im- proving clip fine-tuning performance.

Understanding Museum Exhibits using Vision-Language Reasoning Im- proving clip fine-tuning performance

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Observation 3e1bf36c-3379-4ffa-80aa-57ff33191ce3 · outbound

This paper cites Bam! the behance artistic media dataset for recognition beyond photography.

Understanding Museum Exhibits using Vision-Language Reasoning Bam! the behance artistic media dataset for recognition beyond photography

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Source-reported events for the cited work

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Observation ad4bd166-652f-48e3-a8b3-01743e97da80 · outbound

This paper cites Ask me anything: Free-form vi- sual question answering based on knowledge from external sources.

Understanding Museum Exhibits using Vision-Language Reasoning Ask me anything: Free-form vi- sual question answering based on knowledge from external sources

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c61d608d-9033-4403-bef2-4b8ff3d2fab9 · outbound

This paper cites Language bias in Visual Question Answering: A Survey and Taxonomy.

Understanding Museum Exhibits using Vision-Language Reasoning Language bias in Visual Question Answering: A Survey and Taxonomy

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Observation 9481fe34-7874-4896-803e-f8985656b4b9 · outbound

This paper cites Lit: Zero-shot transfer with locked-image text tuning.

Understanding Museum Exhibits using Vision-Language Reasoning Lit: Zero-shot transfer with locked-image text tuning

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Source-reported events for the cited work

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Observation 6333fb85-8626-4a9a-b17e-2f6f341026e1 · outbound

This paper cites The iMet Collection 2019 Challenge Dataset.

Understanding Museum Exhibits using Vision-Language Reasoning The iMet Collection 2019 Challenge Dataset

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Source-reported events for the cited work

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Observation 6f322d1f-f461-4a98-a33e-59c7ec488409 · outbound

This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

Understanding Museum Exhibits using Vision-Language Reasoning Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

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Source-reported events for the cited work

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Observation 0795a025-0709-4b0d-a524-20aa8616c062 · outbound

This paper cites Extract free dense labels from clip.

Understanding Museum Exhibits using Vision-Language Reasoning Extract free dense labels from clip

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Source-reported events for the cited work

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Observation f0398813-e35d-4c93-a692-fdfb94ee4b49 · outbound

This paper cites Conditional prompt learning for vision-language mod- els.

Understanding Museum Exhibits using Vision-Language Reasoning Conditional prompt learning for vision-language mod- els

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Resolution: unresolved; no resolver link, observed 2026-08-12T04:28:39.722009Z

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Observation 80673caf-d90d-4d30-a895-4da7219100f5 · outbound

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Understanding Museum Exhibits using Vision-Language Reasoning Learning to prompt for vision-language models.In- ternational Journal of Computer Vision, 130(9):2337–2348,

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Observation 7af21584-2ef2-4c9b-88e2-43a4b8a837ac · outbound

This paper cites Detecting twenty-thousand classes using image-level supervision.

Understanding Museum Exhibits using Vision-Language Reasoning Detecting twenty-thousand classes using image-level supervision

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Observation 76d54b7f-72ef-45a1-a906-56d441fc0fe2 · outbound

This paper cites Visual7w: Grounded question answering in images.

Understanding Museum Exhibits using Vision-Language Reasoning Visual7w: Grounded question answering in images

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8433939e-def4-4455-b368-f57453b87aea · outbound

This paper cites LLaVA-Phi: Efficient Multi-Modal Assistant with Small Language Model.

Understanding Museum Exhibits using Vision-Language Reasoning LLaVA-Phi: Efficient Multi-Modal Assistant with Small Language Model

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Observation 9e1d174e-af6e-49ce-ba92-ccce016abe46 · outbound

This paper cites • These aggregators provide access to extensive digitized collections from major museums across Europe and America and offer structured data through platform- specific APIs.

Understanding Museum Exhibits using Vision-Language Reasoning • These aggregators provide access to extensive digitized collections from major museums across Europe and America and offer structured data through platform- specific APIs

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:40.751013Z

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Observation ebe8df6b-8dac-4910-a943-ebc0d528d8f1 · outbound

This paper cites Curation in- volved minimal edits: removing redundant attributes (in- ventory numbers, bibliographic info); extraneous symbols and numbers.

Understanding Museum Exhibits using Vision-Language Reasoning Curation in- volved minimal edits: removing redundant attributes (in- ventory numbers, bibliographic info); extraneous symbols and numbers

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:40.619143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91b95f06-7661-4bdb-bbf8-2a5fae826243 · outbound

This paper cites an unresolved cited work.

Understanding Museum Exhibits using Vision-Language Reasoning Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-12T04:28:40.604256Z

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Observation 68c51512-01ec-4ae6-99e5-3cc2bb9dce3b · outbound

This paper cites Which primary material is the object made of?.

Understanding Museum Exhibits using Vision-Language Reasoning Which primary material is the object made of?

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:28:40.587709Z

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Observation fc021a6f-b5f5-4beb-af3d-2e790aefa241 · outbound

This paper cites For each object, we now have a list of images and a set of question-answer pairs, omitting the answers for which the value is not known.

Understanding Museum Exhibits using Vision-Language Reasoning For each object, we now have a list of images and a set of question-answer pairs, omitting the answers for which the value is not known

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Source-reported events for the cited work

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