Paper Citation Record · LEDGER

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models

As of 19 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 2 inbound Pith citation observations for arXiv:2501.14276.

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

pith.paper-citation-record.v1
2501.14276 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

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measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:51:42.830659Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T11:38:38.471219Z

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation b953dbc5-4c2c-4380-ab0a-4568c43acca0 · outbound

This paper cites Visual instruction tuning.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Visual instruction tuning

Reference 1

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Observation 793e77b5-ae36-4a4e-8708-d4d4d1ad1c04 · outbound

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

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 2

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Observation 06c17ce1-689b-4fa8-a059-5500e98a098d · outbound

This paper cites Sharegpt4v: Improving large multi- modal models with better captions.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Sharegpt4v: Improving large multi- modal models with better captions

Reference 3

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Observation a2eaa09e-434f-4f68-b289-9f04a195ccdd · outbound

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

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Monkey: Image resolution and text label are important things for large multi-modal models

Reference 4

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Observation 4c8928aa-2cb5-4c60-94ad-ace2a76da0d9 · outbound

This paper cites InternLM-XComposer2-4KHD: A pioneering large vision-language model handling resolutions from 336 pixels to 4k HD.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models InternLM-XComposer2-4KHD: A pioneering large vision-language model handling resolutions from 336 pixels to 4k HD

Reference 5

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Observation c450716a-35f9-47b7-9d62-b9aa6d260320 · outbound

This paper cites Salgan: Visual saliency prediction with adversarial networks.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Salgan: Visual saliency prediction with adversarial networks

Reference 6

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Observation 83e6ff61-f14e-45ea-8bab-98341f2d92cb · outbound

This paper cites PaLI: A jointly-scaled multilingual language-image model.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models PaLI: A jointly-scaled multilingual language-image model

Reference 7

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Observation 341bd568-5bef-4ff7-9209-2e8df930f74b · outbound

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

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 8

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Observation 4ac08dd5-2ae7-486a-b65d-a0f9052462d9 · outbound

This paper cites Cambrian-1: A fully open, vision-centric exploration of multimodal LLMs.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Cambrian-1: A fully open, vision-centric exploration of multimodal LLMs

Reference 9

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Observation 0b98434d-d5c4-473f-ab85-df6b0825bfaf · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge, 2024.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Llava-next: Improved reasoning, ocr, and world knowledge, 2024

Reference 10

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Observation 40ac4578-9d36-44c6-bc74-9aac4380cf8c · outbound

This paper cites How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites

Reference 11

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Observation 1365c077-116c-4169-a8d4-ffc36c18a8c9 · outbound

This paper cites Instruct- blip: Towards general-purpose vision-language models with instruction tuning.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Instruct- blip: Towards general-purpose vision-language models with instruction tuning

Reference 12

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Observation 695af14c-d233-47bc-b4bd-f3520a045059 · outbound

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

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Learning transferable visual models from natural language supervision

Reference 13

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Observation 1602eccf-23fe-4268-b4c2-4910c6fce142 · outbound

This paper cites Dual modality prompt tuning for vision-language pre-trained model.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Dual modality prompt tuning for vision-language pre-trained model

Reference 14

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Observation c149faca-a31e-419f-903f-fbdc5d9bfb1c · outbound

This paper cites Sgva-clip: Semantic-guided visual adapting of vision-language mod- els for few-shot image classification.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Sgva-clip: Semantic-guided visual adapting of vision-language mod- els for few-shot image classification

Reference 15

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Observation f3181c5a-fa3f-4cb3-afc8-eff3fb1e2313 · outbound

This paper cites Gpt4ego: Unleashing the potential of pre-trained models for zero- shot egocentric action recognition.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Gpt4ego: Unleashing the potential of pre-trained models for zero- shot egocentric action recognition

Reference 16

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

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding

Reference 17

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This paper cites InternLM2 Technical Report.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models InternLM2 Technical Report

Reference 18

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This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 19

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This paper cites Ocrbench: on the hidden mystery of ocr in large multimodal models.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Ocrbench: on the hidden mystery of ocr in large multimodal models

Reference 20

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Observation 90c802b5-471a-4462-b9a4-4c44ce254abf · outbound

This paper cites Towards vqa models that can read.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Towards vqa models that can read

Reference 21

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Observation fc766ca9-d6d0-4117-a0b4-122ad133a92f · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player? In Proceed- ings of the European Conference on Computer Vision (ECCV) , pages 216–233, 2025.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Mmbench: Is your multi-modal model an all-around player? In Proceed- ings of the European Conference on Computer Vision (ECCV) , pages 216–233, 2025

Reference 22

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Observation 22cb8890-990b-464b-9528-904eec590064 · outbound

This paper cites A diagram is worth a dozen images.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models A diagram is worth a dozen images

Reference 23

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This paper cites Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models

Reference 24

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Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Improved baselines with visual instruction tuning

Reference 25

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Observation 92118df8-568f-47dc-bc9d-2b271f751bbf · outbound

This paper cites Decoupled weight decay regular- ization.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Decoupled weight decay regular- ization

Reference 26

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Observation 54c31879-1f12-40ce-818f-3f1fbf90adc1 · outbound

This paper cites Dvqa: Understanding data visualizations via question answering.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Dvqa: Understanding data visualizations via question answering

Reference 27

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This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models ChartQA: A benchmark for question answering about charts with visual and logical reasoning

Reference 28

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This paper cites Docvqa: A dataset for vqa on document images.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Docvqa: A dataset for vqa on document images

Reference 29

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Observation 423dafa5-36b5-4243-b489-c6c85c98862a · outbound

This paper cites An augmented benchmark dataset for geometric question answering through dual parallel text encoding.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models An augmented benchmark dataset for geometric question answering through dual parallel text encoding

Reference 30

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Observation 3fea2abc-bf7f-49fa-877d-8fc5f3113e0d · outbound

This paper cites Ocr-free document understanding trans- former.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Ocr-free document understanding trans- former

Reference 31

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This paper cites Are we on the right way for evaluating large vision-language models? In Proceedings of the International Conference on Neural Information Processing Systems (NIPS) , 2024.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Are we on the right way for evaluating large vision-language models? In Proceedings of the International Conference on Neural Information Processing Systems (NIPS) , 2024

Reference 32

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This paper cites Mm-vet: evaluating large multimodal models for integrated capabilities.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Mm-vet: evaluating large multimodal models for integrated capabilities

Reference 33

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This paper cites Seed-bench: Benchmarking multimodal large language models.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Seed-bench: Benchmarking multimodal large language models

Reference 34

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Observation c6c03810-9ee8-4a14-8e6e-a27d589e770a · outbound

This paper cites Grok-1.5 vision preview.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Grok-1.5 vision preview

Reference 35

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

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?

Reference 36

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

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models

Reference 37

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Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Infographicvqa

Reference 38

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This paper cites Evaluating object hallucination in large vision-language models.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Evaluating object hallucination in large vision-language models

Reference 39

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Observation dabf7dd9-6e3c-4415-ae52-f3c04b9c7b12 · outbound

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

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:50.238145Z

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This paper cites What matters when building vision-language models? In Proceedings of the International Conference on Neural Information Processing Systems (NIPS), 2024.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models What matters when building vision-language models? In Proceedings of the International Conference on Neural Information Processing Systems (NIPS), 2024

Reference 41

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Observation cc3526a8-d06a-4df2-b2c6-bbb387b08b1c · outbound

This paper cites Vila: On pre-training for visual language models.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Vila: On pre-training for visual language models

Reference 42

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This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 43

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This paper cites InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model

Reference 44

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Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models PaliGemma: A versatile 3B VLM for transfer

Reference 45

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This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models LLaVA-OneVision: Easy Visual Task Transfer

Reference 46

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This paper cites Mini-internvl: a flexible-transfer pocket multi-modal model with 5% parameters and 90% performance.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Mini-internvl: a flexible-transfer pocket multi-modal model with 5% parameters and 90% performance

Reference 47

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This paper cites Ovis: Structural Embedding Alignment for Multimodal Large Language Model.

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models Ovis: Structural Embedding Alignment for Multimodal Large Language Model

Reference 48

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Trust the Model: Compact VLMs as In-Context Judges for Image-Text Data Quality cites this paper.

Trust the Model: Compact VLMs as In-Context Judges for Image-Text Data Quality Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models

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HRVVS: A High-resolution Video Vasculature Segmentation Network via Hierarchical Autoregressive Residual Priors cites this paper.

HRVVS: A High-resolution Video Vasculature Segmentation Network via Hierarchical Autoregressive Residual Priors Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models

Reference 14

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