Paper Citation Record · LEDGER

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion

As of 14 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 2 inbound Pith citation observations for arXiv:2412.04424.

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

pith.paper-citation-record.v1
2412.04424 v1

Coverage vector

measured 49 of 49 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T19:52:01.828238Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation b956506a-982a-431f-8170-27aaaa06b663 · outbound

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

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation 73d66cdb-0fd9-4603-88fb-82d5e93f9db0 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2

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Observation e3dbb1f8-5959-44b1-bc98-6ab0b1857bba · outbound

This paper cites Qwen-vl: A versatile vision-language model for un- derstanding, localization, text reading, and beyond, 2023.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Qwen-vl: A versatile vision-language model for un- derstanding, localization, text reading, and beyond, 2023

Reference 3

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Observation e545521f-1398-4fcb-acaa-3c8b045d2899 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 4

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Observation c2634cb1-deab-4a63-af2c-963f94e4c157 · outbound

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

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Sharegpt4v: Improving large multi-modal models with better captions,

Reference 5

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Observation 4bbbe195-f89a-480e-8f8b-41904477a200 · outbound

This paper cites Are We on the Right Way for Evaluating Large Vision-Language Models?.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 6

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Observation 705a2024-bc8b-4132-88a3-a692ecf4a82a · outbound

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

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 7

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Observation 9414ee1a-b5f6-4f34-8654-3c2d883cd8f5 · outbound

This paper cites RedCaps: web-curated image-text data created by the people, for the people.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion RedCaps: web-curated image-text data created by the people, for the people

Reference 8

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Observation ecdf898e-0441-4504-ada4-9e1fbcfa386e · outbound

This paper cites Davit: Dual attention vision transform- ers.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Davit: Dual attention vision transform- ers

Reference 9

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Observation 65923e19-8764-488a-8f45-b407ced021d8 · outbound

This paper cites MouSi: Poly-Visual-Expert Vision-Language Models.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion MouSi: Poly-Visual-Expert Vision-Language Models

Reference 10

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Observation d85dddf7-2316-4c01-aef1-2e9e7880a900 · outbound

This paper cites Mme: A compre- hensive evaluation benchmark for multimodal large language models, 2024.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Mme: A compre- hensive evaluation benchmark for multimodal large language models, 2024

Reference 11

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Observation 9949e921-a402-44e7-8569-72e9ea923183 · outbound

This paper cites 1 OCRBench ChartQA DocVQA InfoVQA Average Florence-VL 7B 41.4 24.3 44.5 29.4 34.9 OCR 40.9 22.9 44.4 29.0 34.2 (a) Ablation study on OCR features on OCR & Chart benchmark.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion 1 OCRBench ChartQA DocVQA InfoVQA Average Florence-VL 7B 41.4 24.3 44.5 29.4 34.9 OCR 40.9 22.9 44.4 29.0 34.2 (a) Ablation study on OCR features on OCR & Chart benchmark

Reference 12

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Observation 2a861900-2d06-4a04-aac0-5dff573460da · outbound

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

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 13

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Observation 1275089c-6f36-4479-b281-f93268b4aadc · outbound

This paper cites Vizwiz grand challenge: Answering visual questions from blind people.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Vizwiz grand challenge: Answering visual questions from blind people

Reference 14

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Observation 97833a46-8d04-47f4-9ce4-7e47936c38dd · outbound

This paper cites Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate

Reference 15

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Observation 9057387d-297f-4da6-b097-d93f084b186b · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and composi- tional question answering.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Gqa: A new dataset for real-world visual reasoning and composi- tional question answering

Reference 16

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Observation be1c5739-1cc9-4fc8-a3d4-d63acc3e9d10 · outbound

This paper cites https://huggingface.co/datasets/huggingfacem4/docmatix.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion https://huggingface.co/datasets/huggingfacem4/docmatix

Reference 17

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Observation 25e88791-2850-4d96-abf5-cb34e4ed394a · outbound

This paper cites BRAVE: Broadening the visual encoding of vision-language models.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion BRAVE: Broadening the visual encoding of vision-language models

Reference 18

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Observation c81180e8-05f8-4fdf-b406-7af522edb8a9 · outbound

This paper cites A diagram is worth a dozen images.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion A diagram is worth a dozen images

Reference 19

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Observation 69d6e750-e170-4d16-b40f-58e33aa5b120 · outbound

This paper cites Segment anything.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Segment anything

Reference 20

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Observation c18cdb79-ee2e-4d68-80ad-9c318057e82e · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 21

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Observation 1c185393-b516-4b8f-a95a-a7f5e050ab82 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Evaluating Object Hallucination in Large Vision-Language Models

Reference 22

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This paper cites Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Reference 23

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

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Vila: On pre-training for visual language models

Reference 24

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 25

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Visual instruction tuning

Reference 26

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This paper cites MMBench: Is Your Multi-modal Model an All-around Player?.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion MMBench: Is Your Multi-modal Model an All-around Player?

Reference 27

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Observation 66be9e77-1902-4cae-8bfa-ad0a2649ecd9 · outbound

This paper cites On the hidden mystery of ocr in large multimodal models, 2024.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion On the hidden mystery of ocr in large multimodal models, 2024

Reference 28

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 29

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

Reference 31

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Docvqa: A dataset for vqa on document images

Reference 32

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Infographicvqa

Reference 33

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion DINOv2: Learning Robust Visual Features without Supervision

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Learning transferable visual models from natural language supervi- sion

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion High-resolution image synthesis with latent diffusion models

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion High-resolution image synthesis with latent diffusion models

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This paper cites Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Reference 38

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Towards vqa models that can read

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This paper cites From pixels to prose: A large dataset of dense image cap- tions, 2024.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion From pixels to prose: A large dataset of dense image cap- tions, 2024

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

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Observation 791cc9f6-8b4e-42c2-a0d9-998d402dfeb2 · outbound

This paper cites Eyes wide shut? exploring the visual shortcomings of multimodal llms.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Eyes wide shut? exploring the visual shortcomings of multimodal llms

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This paper cites Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models

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This paper cites Grok 1.5v: The next generation of ai.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Grok 1.5v: The next generation of ai

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Florence-2: Advancing a unified representation for a variety of vision tasks

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Vision-flan: Scaling human-labeled tasks in visual instruc- tion tuning, 2024

Reference 46

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This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

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Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi

Reference 48

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Observation 981cc79f-9a2d-4088-ab3c-0344007fd948 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 49

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Pith citing papers

Observation 64004760-0a21-4965-9e0b-4fc9dcc32781 · inbound

FastVLM: Efficient Vision Encoding for Vision Language Models cites this paper.

FastVLM: Efficient Vision Encoding for Vision Language Models Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion

Reference 11

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Observation 218253da-2784-4767-9d0b-c3607ba54cfd · inbound

FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding cites this paper.

FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion

Reference 7

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