Paper Citation Record · LEDGER

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models

As of 10 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2507.22431.

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

pith.paper-citation-record.v1
2507.22431 v1

Coverage vector

measured 45 of 45 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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45 of 45 outbound references displayed

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Outbound references

Observation 8687808d-6b90-469b-859c-9db72dba25ab · outbound

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

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 1

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Observation fdb97316-3ec5-4106-9d2a-27cd89f67a64 · outbound

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

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 2

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Observation e0755a2a-6891-4009-a98a-81960f9c5784 · outbound

This paper cites Internlm-xcomposer2: Mastering free-form text- image composition and comprehension in vision-language large model, 2024.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Internlm-xcomposer2: Mastering free-form text- image composition and comprehension in vision-language large model, 2024

Reference 3

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This paper cites Vlmevalkit: An open-source toolkit for evaluating large multi-modality models.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Vlmevalkit: An open-source toolkit for evaluating large multi-modality models

Reference 4

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Observation da9deaac-5bd3-4c4b-9d39-8fc13fc2bc11 · outbound

This paper cites Improving clip training with language rewrites.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Improving clip training with language rewrites

Reference 5

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Observation 8494b625-6a4d-4b55-ab54-5a42840d6363 · outbound

This paper cites Data fil- tering networks, 2023.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Data fil- tering networks, 2023

Reference 6

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Observation 039d2dd5-6b27-4bc1-963c-aab84da425aa · outbound

This paper cites Eva: Exploring the limits of masked visual representa- tion learning at scale.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Eva: Exploring the limits of masked visual representa- tion learning at scale

Reference 7

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Observation 67bff9d6-c2f2-466e-b1f4-172f4223377b · outbound

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

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Mme: A compre- hensive evaluation benchmark for multimodal large language models, 2024

Reference 8

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Observation 08175ae1-0b0d-48a8-a002-ca6b7aa2389d · outbound

This paper cites Datacomp: In search of the next generation of multimodal datasets, 2023.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Datacomp: In search of the next generation of multimodal datasets, 2023

Reference 9

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Observation 7262f8be-d511-49d4-9f5f-7e83a661c015 · outbound

This paper cites Classification done right for vision-language pre- training.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Classification done right for vision-language pre- training

Reference 10

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Observation 911d6cae-6135-43ee-abee-333269196a7d · outbound

This paper cites GPT-4o System Card.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models GPT-4o System Card

Reference 11

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Observation f54851e5-6dbb-4eb5-a7ed-24e0d60f14d3 · outbound

This paper cites Open- clip, 2021.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Open- clip, 2021

Reference 12

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Observation f0fba9fa-74f1-4a7d-b16e-d53deb8a3785 · outbound

This paper cites Veclip: Improving clip training via visual-enriched captions,.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Veclip: Improving clip training via visual-enriched captions,

Reference 13

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Observation 372feb51-e920-4792-a9ab-c9d3bc5b0ee6 · outbound

This paper cites Seed-bench: Benchmarking multimodal llms with generative comprehension, 2023.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Seed-bench: Benchmarking multimodal llms with generative comprehension, 2023

Reference 14

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Observation 77ed29c0-809d-412b-8fff-de2ece218112 · outbound

This paper cites Grounded language-image pre-training.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Grounded language-image pre-training

Reference 15

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Observation 3fe468b9-21f8-477f-980b-b93aadf25fa3 · outbound

This paper cites Clipa-v2: Scaling clip training with 81.17, 11.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Clipa-v2: Scaling clip training with 81.17, 11

Reference 16

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Observation bca85c64-9114-4285-a012-c084a8a858ba · outbound

This paper cites What If We Recaption Billions of Web Images with LLaMA-3?.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models What If We Recaption Billions of Web Images with LLaMA-3?

Reference 17

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This paper cites Improved baselines with visual instruction tuning, 2023.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Improved baselines with visual instruction tuning, 2023

Reference 18

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This paper cites Visual instruction tuning, 2023.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Visual instruction tuning, 2023

Reference 19

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Observation 60a38ff6-e757-4a52-91d5-6debf7111c78 · outbound

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

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 20

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Observation ab674b3e-c7a2-46c9-84cb-9726f7a5cfc4 · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player?, 2024.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Mmbench: Is your multi-modal model an all-around player?, 2024

Reference 21

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Observation bae38e45-a8ab-44c0-866e-28cd93456e8a · outbound

This paper cites Slip: Self-supervision meets language-image pre- training.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Slip: Self-supervision meets language-image pre- training

Reference 22

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Observation 51154b03-b426-4fd2-bc89-38e3bcd0ef82 · outbound

This paper cites Improving multimodal datasets with image captioning.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Improving multimodal datasets with image captioning

Reference 23

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Observation 1a6f594c-487c-42a3-be44-5dbf127470de · outbound

This paper cites Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever

Reference 24

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Observation 21558c4b-2ba8-43ad-a5c6-19f6ce3b1c71 · outbound

This paper cites Denseclip: Language-guided dense prediction with context- aware prompting.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Denseclip: Language-guided dense prediction with context- aware prompting

Reference 25

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Observation d1b1a601-d1dd-4e87-a7f7-aaddb8730b77 · outbound

This paper cites Fusecap: Leveraging large language mod- els for enriched fused image captions.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Fusecap: Leveraging large language mod- els for enriched fused image captions

Reference 26

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Observation 972fd9d1-1252-4f40-af1c-da919df8a99f · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 27

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Observation 3d5e3a2d-5cc3-4ce9-839f-03e51b64573c · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 28

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Observation 41e5be2f-a1ec-40fe-b8be-6310f94a4b85 · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 29

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Observation b64dc236-7bd4-4c47-b9e5-56161efecd1a · outbound

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

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 30

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Observation 2e124188-8a4c-472c-8dab-0138ecb71694 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 31

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Observation 54248436-f71d-4947-a733-fae403771130 · outbound

This paper cites Locca: Vi- sual pretraining with location-aware captioners.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Locca: Vi- sual pretraining with location-aware captioners

Reference 32

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Observation 1fbfab01-1955-4999-a42c-e42b7267011d · outbound

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

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 33

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Observation 2335abde-285a-4cac-8447-b1a2c9cbfa25 · outbound

This paper cites VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

Reference 34

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Observation 04d2f84a-85a6-43b4-b30b-3156a9177e25 · outbound

This paper cites Demystifying CLIP Data.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Demystifying CLIP Data

Reference 35

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Observation 8a8a5c90-6af3-4ba7-85d2-b63b449ccf57 · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models, 2022.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Coca: Contrastive captioners are image-text foundation models, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:46.148854Z

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Observation 85b5c121-2c3c-4e77-9e74-d93c9127a5d4 · outbound

This paper cites CapsFusion: Rethinking Image-Text Data at Scale.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models CapsFusion: Rethinking Image-Text Data at Scale

Reference 37

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Observation c059c51c-33df-4d19-b842-cd199730cd5a · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understand- ing and reasoning benchmark for expert agi.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Mmmu: A massive multi-discipline multimodal understand- ing and reasoning benchmark for expert agi

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:46.116925Z

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Observation 8b7f51ce-0d25-44d5-9ed3-2353ba1dacc2 · outbound

This paper cites When and why vision- language models behave like bags-of-words, and what to do about it? In International Conference on Learning Repre- sentations, 2023.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models When and why vision- language models behave like bags-of-words, and what to do about it? In International Conference on Learning Repre- sentations, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:46.091776Z

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Observation 9669d846-da58-4512-9db6-0f32adf4edfc · outbound

This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 40

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This paper cites Sigmoid loss for language image pre-training.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Sigmoid loss for language image pre-training

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:46.069538Z

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Observation 8f631553-cbb2-41ca-b062-ad3de7d05214 · outbound

This paper cites Long-clip: Unlocking the long-text capability of clip.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Long-clip: Unlocking the long-text capability of clip

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:46.041210Z

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Observation 4adfeb5e-0eca-42d0-8e52-8e9c2088bd60 · outbound

This paper cites Glipv2: Unifying localiza- tion and vision-language understanding.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Glipv2: Unifying localiza- tion and vision-language understanding

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:46.010744Z

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Observation d794a3e4-5197-44fc-963e-5adfd3f1bf62 · outbound

This paper cites Training setup and dataset scale.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Training setup and dataset scale

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:45.988293Z

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Observation 72ead4ff-f661-4f49-ac37-0df59345b06e · outbound

This paper cites Examples We present some examples from the acquired dataset.

HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models Examples We present some examples from the acquired dataset

Reference 45

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Pith citing papers

No inbound Pith citation observations are available.