HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? | Paper Citation Record

Observation f9b1899d-2417-4785-8d5f-28b113856ef8 · outbound

This paper cites 12 Kucherlapati Raju 13, Genome data analysis: Baylor College of Medicine Creighton Chad J.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? 12 Kucherlapati Raju 13, Genome data analysis: Baylor College of Medicine Creighton Chad J

Reference 1

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Observation 4d0c885f-e035-4a56-9bb4-5492d0030c35 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 2

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Observation 1175f303-e6a7-4395-97ef-3c9cd122ee9e · outbound

This paper cites GPT-4 Technical Report.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? GPT-4 Technical Report

Reference 3

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This paper cites The claude 3 model family: Opus, sonnet, haiku.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? The claude 3 model family: Opus, sonnet, haiku

Reference 4

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Observation 15c957c5-8cd7-4cd8-bd1a-b94bde5faa64 · outbound

This paper cites Bach: Grand challenge on breast cancer histology im- ages.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Bach: Grand challenge on breast cancer histology im- ages

Reference 5

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Observation dd92b8e1-1e15-4332-b442-9ff0bc14226d · outbound

This paper cites Jauregui, and Juan Andr ´es Cardoso.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Jauregui, and Juan Andr ´es Cardoso

Reference 6

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Observation d3a4f4f7-944b-474c-995c-f7149645fcae · outbound

This paper cites Ef- ficient high-resolution deep learning: A survey.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Ef- ficient high-resolution deep learning: A survey

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.682253Z

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Observation 19bb4625-5def-4fbf-9d05-ce0b6ba78736 · outbound

This paper cites Effi- cient high-resolution deep learning: A survey.ACM Comput.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Effi- cient high-resolution deep learning: A survey.ACM Comput

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.672630Z

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Observation e944eaa2-510a-49db-9955-3c21580f39a6 · outbound

This paper cites Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.419841Z

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Observation 888b5071-0d94-441c-bd07-3b0a4ffc64c3 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.423560Z

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Observation e6070e0a-7b27-4f7e-a792-7a5bf964a236 · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.427409Z

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Observation e97359c0-4da3-485b-8341-aa52329fbaa5 · outbound

This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.663099Z

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Observation 16647775-f89e-440e-b7ef-ec1659f534dd · outbound

This paper cites Functional map of the world.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Functional map of the world

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.652996Z

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Observation c6c8c29c-eb5e-4f8d-89e7-bfb21486adfd · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? The cityscapes dataset for semantic urban scene understanding

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.642930Z

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Observation 57a333d4-255e-4b52-86fb-ddd5aea2bf8f · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 15

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Observation e34ee8a0-b7f5-476e-b3ed-e84af2704903 · outbound

This paper cites LungHist700: A dataset of histological images for deep learning in pulmonary pathology.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? LungHist700: A dataset of histological images for deep learning in pulmonary pathology

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.632512Z

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Observation b299ab0e-d64d-4bbd-87ba-8cadd9c49f01 · outbound

This paper cites LungHist700: A dataset of histological images for deep learning in pulmonary pathology.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? LungHist700: A dataset of histological images for deep learning in pulmonary pathology

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.621966Z

Source-reported events for the cited work

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Observation ab042208-0d94-4f79-8ecc-e81ad17c7e34 · outbound

This paper cites Internlm-xcomposer2-4khd: A pioneer- ing large vision-language model handling resolutions from 336 pixels to 4k HD.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Internlm-xcomposer2-4khd: A pioneer- ing large vision-language model handling resolutions from 336 pixels to 4k HD

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.611682Z

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Observation a263f2d1-568b-4dbf-b65f-3d6ffda9c112 · outbound

This paper cites The Llama 3 Herd of Models.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? The Llama 3 Herd of Models

Reference 19

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Observation 3543c1bb-b91e-41ad-a5a0-6f8843f862db · outbound

This paper cites Describing differences in image sets with natural language.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Describing differences in image sets with natural language

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.601042Z

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Observation f65c327e-f379-4aad-89a6-efc6604d2669 · outbound

This paper cites Floorplancad: A large-scale cad draw- ing dataset for panoptic symbol spotting.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Floorplancad: A large-scale cad draw- ing dataset for panoptic symbol spotting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.590413Z

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Observation b33ecc19-b636-4868-807b-f9f156c0d2a3 · outbound

This paper cites Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.465714Z

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Observation 67467734-d1fc-46cc-8cea-14e87c0e9ea4 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 23

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Observation 5dae7f81-ff1f-428d-8965-120282bb8756 · outbound

This paper cites Llava-uhd: An LMM perceiving any aspect ratio and high- resolution images.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Llava-uhd: An LMM perceiving any aspect ratio and high- resolution images

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.569489Z

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Observation a7cb86e0-1b89-49ee-8103-6ee52159a44f · outbound

This paper cites Cogagent: A visual lan- guage model for GUI agents.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Cogagent: A visual lan- guage model for GUI agents

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.558847Z

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Observation e71e3d52-d4ac-49ac-bb8c-d59361ed0628 · outbound

This paper cites mplug-docowl 1.5: Unified structure learning for ocr-free document understanding.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? mplug-docowl 1.5: Unified structure learning for ocr-free document understanding

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.548052Z

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Observation c82a385b-598a-4c8f-87ef-8d3fcbdbcdda · outbound

This paper cites mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding

Reference 27

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Observation 694fe1a0-1e68-4e9d-a306-5ab338b52b13 · outbound

This paper cites Novachart: A large- scale dataset towards chart understanding and generation of multimodal large language models.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Novachart: A large- scale dataset towards chart understanding and generation of multimodal large language models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.538004Z

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

Observation 320bb115-4ebc-421b-a50b-ed91ba62d796 · outbound

This paper cites High reso- lution image quality database.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? High reso- lution image quality database

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.527667Z

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Observation dbd2eaae-922f-4a31-b77a-d201cdb248a9 · outbound

This paper cites Mini-monkey: Alleviating the semantic saw- tooth effect for lightweight MLLMs via complementary im- age pyramid.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Mini-monkey: Alleviating the semantic saw- tooth effect for lightweight MLLMs via complementary im- age pyramid

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.517732Z

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Observation a3c99a96-fffb-4241-a0cb-e6cceaf6ca5c · outbound

This paper cites The apolloscape dataset for autonomous driving.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? The apolloscape dataset for autonomous driving

Reference 31

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Observation f5021136-94e3-4344-99ca-29f1255b301d · outbound

This paper cites GPT-4o System Card.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? GPT-4o System Card

Reference 32

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Observation 47e467f5-93b5-46f2-8aa4-3c00497d8f1d · outbound

This paper cites Multi-source multi-scale counting in extremely dense crowd images.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Multi-source multi-scale counting in extremely dense crowd images

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.496564Z

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Observation 7771275f-05a5-40d4-b808-9a8c39b60bd7 · outbound

This paper cites Composition loss for counting, density map estima- tion and localization in dense crowds.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Composition loss for counting, density map estima- tion and localization in dense crowds

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.486389Z

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Observation bbdc62ea-4066-464f-9852-4d5b711ee143 · outbound

This paper cites CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging

Reference 35

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Observation af02aa70-aabd-4317-b138-8e2890980087 · outbound

This paper cites MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 36

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Observation f97d40ff-5c0e-4917-8a81-08ae1b554f3b · outbound

This paper cites A diagram is 10 worth a dozen images.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? A diagram is 10 worth a dozen images

Reference 37

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Observation ef01dd84-2979-419b-b91b-f2006978501c · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross B.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross B

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.463325Z

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Observation 652cbbda-5d7a-4ae4-b779-259e86ac5003 · outbound

This paper cites an unresolved cited work.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Unresolved cited work

Reference 39

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Observation ce38ad38-cf11-496e-b34a-23bafbf7087c · outbound

This paper cites A dataset of clinically generated visual questions and answers about radiology images.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? A dataset of clinically generated visual questions and answers about radiology images

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.441274Z

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Observation dd87e033-17dd-40d5-a7ac-b5ca145bb28e · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation, 2022.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation, 2022

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.431021Z

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Observation 090ce644-3b98-469d-9077-51d19d9bb127 · outbound

This paper cites Hrvqa: A visual question answering benchmark for high-resolution aerial images.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Hrvqa: A visual question answering benchmark for high-resolution aerial images

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.420747Z

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

Observation 5ea0ebcb-a443-4f56-bce3-67b7cd2542a7 · outbound

This paper cites To- wards streaming perception.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? To- wards streaming perception

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.410588Z

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Observation e02da5d8-5072-4c2c-b1f7-49c0d86fa976 · outbound

This paper cites Mon- key: Image resolution and text label are important things for large multi-modal models.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Mon- key: Image resolution and text label are important things for large multi-modal models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.399929Z

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Observation 8834e4b0-29c1-49bb-ac17-2cf49e132b87 · outbound

This paper cites The ArtBench Dataset: Benchmarking Generative Models with Artworks.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? The ArtBench Dataset: Benchmarking Generative Models with Artworks

Reference 45

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Source-reported events for the cited work

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Observation 702d130a-c5fd-47a4-bb23-c923f4826bf1 · outbound

This paper cites Visual instruction tuning.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Visual instruction tuning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.389280Z

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 b662ac6b-21e7-47e4-91a6-3b735978b878 · outbound

This paper cites Visual instruction tuning.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Visual instruction tuning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.378720Z

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 5c0f2fe2-da3d-4828-b7db-7357e9005fd2 · outbound

This paper cites Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.368266Z

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 f5d8e1cc-4b44-4e31-8afd-14b7e3d78322 · outbound

This paper cites A convnet for the 2020s.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? A convnet for the 2020s

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.357129Z

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 f3b305db-fadf-4584-b15e-fa91c6b88242 · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.563373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 997a93e9-ecbe-44a5-9a1d-bf4f0c1d7d36 · outbound

This paper cites Feast your eyes: Mixture- of-resolution adaptation for multimodal large language mod- els.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Feast your eyes: Mixture- of-resolution adaptation for multimodal large language mod- els

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.344940Z

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 432380a8-118f-4daf-b63d-c7788b99631b · outbound

This paper cites Infographicvqa.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Infographicvqa

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.333675Z

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 304adae2-21fe-4aed-8c1e-cadeca871c33 · outbound

This paper cites MM1: methods, analysis and insights from multimodal LLM pre-training.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? MM1: methods, analysis and insights from multimodal LLM pre-training

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.322446Z

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 36d208a9-8be8-4387-b4e5-9c87de2704f5 · outbound

This paper cites The mame dataset: on the relevance of high resolution and variable shape image properties.Applied Intelligence, 52(10):11703–11724, 2022.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? The mame dataset: on the relevance of high resolution and variable shape image properties.Applied Intelligence, 52(10):11703–11724, 2022

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.311099Z

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 6d458e48-6f4a-4836-86b6-9d15bbb56e64 · outbound

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

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Learning transferable visual models from natural language supervision

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.300422Z

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 418f9234-1f9a-48e7-b5ec-48bed3326402 · outbound

This paper cites The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.584823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dbd83c3-87e4-46ae-bc16-6a5df342a570 · outbound

This paper cites Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.281672Z

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 2effe67c-8df2-4ba9-be1d-f9082195663a · outbound

This paper cites MileBench: Benchmarking MLLMs in Long Context.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? MileBench: Benchmarking MLLMs in Long Context

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.591664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2249752f-28cf-48b5-afd6-ee52df310d5b · outbound

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

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Gemini: A Family of Highly Capable Multimodal Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.595291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0073b7ca-a1b8-42e1-bd00-913a855c079c · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.599306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8d37346-5381-4628-8b22-f894d82d57e9 · outbound

This paper cites Predicting breast tumor proliferation from whole-slide im- ages: the tupac16 challenge.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Predicting breast tumor proliferation from whole-slide im- ages: the tupac16 challenge

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.270358Z

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 3581a251-a555-4e0f-81f8-bd8810a09e88 · outbound

This paper cites MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.606264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8038171f-0e4d-4270-88fd-fe9b7f7436c3 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.613989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99e3a955-7b77-43ed-a0d4-4de8baafce46 · outbound

This paper cites Divide, conquer and combine: A training-free framework for high-resolution im- age perception in multimodal large language models.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Divide, conquer and combine: A training-free framework for high-resolution im- age perception in multimodal large language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.258765Z

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 99dd5e4f-374c-4d32-bc37-8bb84f7c2773 · outbound

This paper cites Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.620905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4cab3b4-c321-46b0-9e4b-28dae3ee7bd5 · outbound

This paper cites Needle in a multimodal haystack.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Needle in a multimodal haystack

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.247840Z

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 eef52f33-274d-440e-b097-3312e6487ebe · outbound

This paper cites Panda: A gigapixel- level human-centric video dataset.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Panda: A gigapixel- level human-centric video dataset

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.236547Z

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 57526d2d-fb65-4948-b8c8-6d0d0a0fe354 · outbound

This paper cites Weiser, P.L.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Weiser, P.L

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.225206Z

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 2c891846-3d79-4421-aae7-cdf56950113c · outbound

This paper cites V?: Guided visual search as a core mechanism in multimodal llms.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? V?: Guided visual search as a core mechanism in multimodal llms

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.203540Z

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 d38f6978-372a-4e46-914f-b76d9b486cff · outbound

This paper cites Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.641231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88fce6b9-61f5-46f8-b8c4-77343d86eccb · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.648406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8fdb897-c59f-4dad-b020-0499b7480244 · outbound

This paper cites Deep learning for detecting corona virus disease 2019 (covid-19) on high-resolution computed tomography: a pilot study.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Deep learning for detecting corona virus disease 2019 (covid-19) on high-resolution computed tomography: a pilot study

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.192182Z

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 319de3f4-0f78-4693-ad79-70d0235c154c · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.181330Z

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 a3770264-13b9-43f9-a15f-822ebf6caee3 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.659019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6e95b13-d20a-4042-9b3e-21ee90b4f30c · outbound

This paper cites Single-image crowd counting via multi-column convolutional neural network.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Single-image crowd counting via multi-column convolutional neural network

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.162943Z

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 4ff3db0e-5e4a-45ea-8d17-2c8a00b98a54 · outbound

This paper cites LLaVA-Video: Video Instruction Tuning With Synthetic Data.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? LLaVA-Video: Video Instruction Tuning With Synthetic Data

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.665965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce0ae181-b1cf-4695-9ff0-773db659f6a4 · outbound

This paper cites MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.669629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ad8a1ae-f2b4-4ff2-accb-87d5da2029f5 · outbound

This paper cites Monitoring Extracted from MME-Realworld, this dataset features images taken from public safety cameras in diverse scenarios.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Monitoring Extracted from MME-Realworld, this dataset features images taken from public safety cameras in diverse scenarios

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.151619Z

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 f10ccd4a-4de5-4ce5-a358-e5474c129936 · outbound

This paper cites an unresolved cited work.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:22:53.139626Z

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 c18d8af0-22b6-4773-8268-b177c9265cec · outbound

This paper cites Phi- 3.5 [2] is a lightweight model designed for efficient lan- guage understanding and generation.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Phi- 3.5 [2] is a lightweight model designed for efficient lan- guage understanding and generation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.128132Z

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 4439af5e-c13a-4957-b8fe-e865bc6755f0 · outbound

This paper cites The scores are the average per- formance of all samples in val, test, testmini splits.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? The scores are the average per- formance of all samples in val, test, testmini splits

Reference 84

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T10:22:53.003484Z

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 ea35b337-831a-47a2-a900-74d0eeac204d · outbound

This paper cites question n Give an answer with this format: <ans>ANSWER</ans>, no redundant words. For example: <ans>A</ans>.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? question n Give an answer with this format: <ans>ANSWER</ans>, no redundant words. For example: <ans>A</ans>

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:52.992279Z

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 4b9f70eb-5177-4308-8a37-d78e5fd7e01a · outbound

This paper cites We compress the images to display them in the paper.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? We compress the images to display them in the paper

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:52.980470Z

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 a0c9f485-4bef-49bc-b1b0-d659d904e87f · outbound

This paper cites Geological Survey data release, 2022.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? Geological Survey data release, 2022

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:22:53.214714Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers