Paper Citation Record · LEDGER

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks

As of 15 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 3 inbound Pith citation observations for arXiv:2411.16749.

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

pith.paper-citation-record.v1
2411.16749 v2

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:03:29.034095Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:02:55.814289Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T07:39:50.478310Z

Reference resolution

72 of 72 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c692dc30-8d04-46fc-bdb9-69b11c94c281 · outbound

This paper cites Qwen Technical Report.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Qwen Technical Report

Reference 1

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Observation 05e9e2ff-15f9-4df0-9a12-3885a126b03b · outbound

This paper cites eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 2

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Observation 12fd3cd8-e7a3-4c30-ac7b-2e13521fdd51 · outbound

This paper cites Zero-shot composed image retrieval with textual inversion, 2023.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Zero-shot composed image retrieval with textual inversion, 2023

Reference 3

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Observation 1fceac45-1586-4f64-9669-e0b3e690454e · outbound

This paper cites Murphy, William T.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Murphy, William T

Reference 4

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Observation f9676e7d-26e8-41a9-ace2-ad83eeb116f1 · outbound

This paper cites Training-Free Layout Control with Cross-Attention Guidance.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Training-Free Layout Control with Cross-Attention Guidance

Reference 5

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AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Unresolved cited work

Reference 6

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Observation 86fb60b6-fb77-4d46-ad0e-0327c9ca0e31 · outbound

This paper cites Auto cherry-picker: Learning from high-quality generative data driven by lan- guage, 2024.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Auto cherry-picker: Learning from high-quality generative data driven by lan- guage, 2024

Reference 7

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Observation 57cbed7a-a7c8-4af1-95c7-dad21678ad53 · outbound

This paper cites Style injec- tion in diffusion: A training-free approach for adapting large- scale diffusion models for style transfer.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Style injec- tion in diffusion: A training-free approach for adapting large- scale diffusion models for style transfer

Reference 8

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Observation 7fbaf30c-0d5d-4a4f-b529-a8115df8bdb5 · outbound

This paper cites Roboflow 100: A rich, multi-domain object detection benchmark, 2022.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Roboflow 100: A rich, multi-domain object detection benchmark, 2022

Reference 9

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Observation 725a48ba-ac3f-4282-aa4a-a4760ac979ef · outbound

This paper cites Diffusion models beat gans on image synthesis, 2021.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Diffusion models beat gans on image synthesis, 2021

Reference 10

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Observation 2b2d9cbc-24d2-422d-8402-23e9f07a5016 · outbound

This paper cites Hierarchical text-conditional image generation with clip latents, 2022.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Hierarchical text-conditional image generation with clip latents, 2022

Reference 11

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Observation 4331ec7b-9037-4d7d-9f39-83d9892c0d0b · outbound

This paper cites Everingham, L.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Everingham, L

Reference 12

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Observation 3bfac62c-0fa0-45e1-b224-9716fe6e27ea · outbound

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

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Mme: A compre- hensive evaluation benchmark for multimodal large language models, 2024

Reference 13

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Observation 4bb681df-7e46-47aa-895e-db80bd997c94 · outbound

This paper cites Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector

Reference 15

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Observation b384a23f-7d64-4873-825a-a005b7c34e3d · outbound

This paper cites Apollo home page.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Apollo home page

Reference 16

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Observation fc22c40a-94d4-4411-ab47-e9b6483d592e · outbound

This paper cites Classifier-Free Diffusion Guidance.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Classifier-Free Diffusion Guidance

Reference 17

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Observation 5d6d2f82-53ec-40de-b0ed-6d409125ef26 · outbound

This paper cites Denoising dif- fusion probabilistic models.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Denoising dif- fusion probabilistic models

Reference 18

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Observation b98fe489-8dcc-4ac2-9b0f-810d4a896028 · outbound

This paper cites Egtr: Extracting graph from trans- former for scene graph generation.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Egtr: Extracting graph from trans- former for scene graph generation

Reference 19

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Observation 4fa718c8-11e7-49a5-9a2f-cc964bfb94a5 · outbound

This paper cites Cross-domain weakly-supervised object de- tection through progressive domain adaptation.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Cross-domain weakly-supervised object de- tection through progressive domain adaptation

Reference 20

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Observation 9fea8582-4a9d-4cba-be0c-4c3f05fe742c · outbound

This paper cites YOLOv5 by Ultralytics.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks YOLOv5 by Ultralytics

Reference 21

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Observation 0bd6db3c-721e-42d1-82a3-881ec9ed0645 · outbound

This paper cites FOCUS: Familiar objects in common and uncommon settings.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks FOCUS: Familiar objects in common and uncommon settings

Reference 22

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Observation d1e5a5f1-0f2c-4d5a-9c33-08189376a4d2 · outbound

This paper cites Segment Anything.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Segment Anything

Reference 23

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Observation d990bf1c-0b0f-4314-8820-2da0ebba2b21 · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 24

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Observation 6604a584-c5e4-4b53-aa79-057edbbcdd07 · outbound

This paper cites ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

Reference 25

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Observation 94809d2d-bf34-453e-acf5-884c507b947c · outbound

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

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 26

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This paper cites Grounded language-image pre-training.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Grounded language-image pre-training

Reference 27

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AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Gligen: Open-set grounded text-to-image generation

Reference 28

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This paper cites Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy

Reference 29

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This paper cites Caphuman: Capture your moments in parallel uni- verses.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Caphuman: Capture your moments in parallel uni- verses

Reference 30

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This paper cites Lawrence Zitnick, and Piotr Doll ´ar.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Lawrence Zitnick, and Piotr Doll ´ar

Reference 31

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Observation 868fa835-a716-4f6f-9411-8aee9c97ffde · outbound

This paper cites DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning

Reference 32

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

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Improved baselines with visual instruction tuning, 2023

Reference 33

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AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Visual instruction tuning, 2023

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

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 35

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This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 36

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Observation c0510e0d-9415-4383-9f60-4f2291ab2036 · outbound

This paper cites Image retrieval on real-life images with pre-trained vision-and-language models.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Image retrieval on real-life images with pre-trained vision-and-language models

Reference 37

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Observation 92301f46-ef64-48b7-b9d5-80f8c10b6a9f · outbound

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

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Ovis: Structural Embedding Alignment for Multimodal Large Language Model

Reference 38

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This paper cites Glide: Towards photorealistic image generation and editing with text-guided diffusion models, 2022.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Glide: Towards photorealistic image generation and editing with text-guided diffusion models, 2022

Reference 39

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AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Gpt-4 technical report, 2023

Reference 40

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

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever

Reference 41

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Observation 77bdaf5e-5f3f-411d-a735-ea8d85940848 · outbound

This paper cites Anwer, Eric Xing, Ming-Hsuan Yang, and Fahad S.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Anwer, Eric Xing, Ming-Hsuan Yang, and Fahad S

Reference 42

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Observation 87495f66-569f-4ae2-9092-b39838d75d2b · outbound

This paper cites Generative ad- versarial text to image synthesis, 2016.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Generative ad- versarial text to image synthesis, 2016

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:32.547836Z

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Observation 1d5a05aa-53b5-45ad-a7fb-bb717b5f2590 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models, 2021.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks High-resolution image syn- thesis with latent diffusion models, 2021

Reference 44

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Observation b9275d70-af62-44e8-a70a-15ef44e87a99 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 45

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This paper cites Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, Seyedeh Sara Mahdavi, Raphael Gontijo Lopes, Tim Salimans, Jonathan Ho, David Fleet, and Mohammad Norouzi.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, Seyedeh Sara Mahdavi, Raphael Gontijo Lopes, Tim Salimans, Jonathan Ho, David Fleet, and Mohammad Norouzi

Reference 46

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Observation 05aeeb2c-1ae7-460e-b985-211d8ff29cea · outbound

This paper cites Denoising Diffusion Implicit Models.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Denoising Diffusion Implicit Models

Reference 47

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This paper cites Gen2det: Generate to detect, 2023.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Gen2det: Generate to detect, 2023

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:32.384266Z

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Observation 44feffb4-403d-4823-a13d-286d07063256 · outbound

This paper cites Effective data augmentation with diffusion models, 2023.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Effective data augmentation with diffusion models, 2023

Reference 49

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Observation 630417ef-43f3-4def-bb5e-3f5a037b4189 · outbound

This paper cites Attention is all you need.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Attention is all you need

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c230471-ae9e-4077-bead-c08e9c77ba6b · outbound

This paper cites YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:32.170435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a2c6d3a8-2416-485a-a91f-d8128744806a · outbound

This paper cites Learning from synthetic data for crowd counting in the wild, 2019.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Learning from synthetic data for crowd counting in the wild, 2019

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:32.106032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d2519556-a068-41a8-b835-fa463d309491 · outbound

This paper cites Instancediffusion: Instance-level control for image generation, 2024.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Instancediffusion: Instance-level control for image generation, 2024

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:28.493864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 514d7a75-729a-4785-8607-2ed0d3bb1d8b · outbound

This paper cites Fine-grained prototypes distillation for few-shot object de- tection.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Fine-grained prototypes distillation for few-shot object de- tection

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.893574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6cdd4fc8-b4e4-4bd1-b7d3-b37a6a950138 · outbound

This paper cites ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:28.547482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 097caa9f-a2f3-4d66-80f5-74b614ede8d3 · outbound

This paper cites Cashman, and Jamie Shotton.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Cashman, and Jamie Shotton

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.688006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1fcc220b-2b9f-48d7-b0a9-9b329eaf17c0 · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:28.567989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a1387d4-dfc1-454c-b7a9-08eb179b69c8 · outbound

This paper cites BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:28.583234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c6a7ab-555b-45ce-ac4b-dc398d456c8e · outbound

This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation, 2023.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Imagere- ward: Learning and evaluating human preferences for text- to-image generation, 2023

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.579976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f36b568b-2786-4b0d-ab84-207ac6ce8456 · outbound

This paper cites Attngan: Fine- grained text to image generation with attentional generative adversarial networks, 2017.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Attngan: Fine- grained text to image generation with attentional generative adversarial networks, 2017

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.525743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6a75bd0a-58ae-4df3-b0bc-088e5495643f · outbound

This paper cites Ldre: Llm-based divergent reasoning and ensemble for zero-shot composed image re- trieval.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Ldre: Llm-based divergent reasoning and ensemble for zero-shot composed image re- trieval

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.491714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cefe5213-571d-4075-85e9-fa4563dec278 · outbound

This paper cites GLIPv2: Unifying Localization and Vision-Language Understanding.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks GLIPv2: Unifying Localization and Vision-Language Understanding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:28.738271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12682082-1eb5-40a3-bdd7-f5849f50a3a0 · outbound

This paper cites Dynrefer: Delving into region-level multi-modality tasks via dynamic resolution,.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Dynrefer: Delving into region-level multi-modality tasks via dynamic resolution,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.327190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cf6ff3b9-0cc2-4568-b37e-683e08f0dc1c · outbound

This paper cites Layoutdiffusion: Controllable diffu- sion model for layout-to-image generation.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Layoutdiffusion: Controllable diffu- sion model for layout-to-image generation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.221819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 276af15a-9816-481e-b84d-d75d68c2fa70 · outbound

This paper cites Pyramid Diffusion Models For Low-light Image Enhancement.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Pyramid Diffusion Models For Low-light Image Enhancement

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:28.852307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f14d6669-2162-4006-80e6-e51d6e6be41b · outbound

This paper cites Migc: Multi-instance generation controller for text-to-image synthesis.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Migc: Multi-instance generation controller for text-to-image synthesis

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.185926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f09b7ed7-b6f1-431f-b1df-c58e619096fb · outbound

This paper cites MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:28.875664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7caf1579-74f0-4b62-9afd-110d02338df8 · outbound

This paper cites 3dis: Depth-driven decoupled instance synthesis for text-to-image generation.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks 3dis: Depth-driven decoupled instance synthesis for text-to-image generation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T14:03:28.932870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 220c78ab-c58c-4ee3-824c-44ce1abc0362 · outbound

This paper cites Odgen: Domain-specific object detection data generation with diffusion models, 2024.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Odgen: Domain-specific object detection data generation with diffusion models, 2024

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.111447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8770c905-370a-4bcc-b38b-b0fb47b48218 · outbound

This paper cites Imagine a basic scene, including whether the scene is near or far, and give a scene label, such as on the grass or in the hospital.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Imagine a basic scene, including whether the scene is near or far, and give a scene label, such as on the grass or in the hospital

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:31.051485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d62d14f-82bd-46ae-9124-c91781b2037c · outbound

This paper cites users may provide reference images and modify the content of the reference images.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks users may provide reference images and modify the content of the reference images

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:30.904796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95af2ccc-a133-4f01-aef9-57dc3d639933 · outbound

This paper cites an unresolved cited work.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:03:30.813039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8a5bc7cf-b773-40af-9680-26cedd6cc491 · outbound

This paper cites Summarize output in format: … Make sure that each layout contains only one instance.

AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks Summarize output in format: … Make sure that each layout contains only one instance

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:03:30.744950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 33eaaa53-e5a5-4bf7-b5de-37e8634de503 · inbound

Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy cites this paper.

Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T14:02:55.814289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52e3cd3d-fc9b-450e-9da5-55576307077c · inbound

FoleyDirector: Fine-Grained Temporal Steering for Video-to-Audio Generation via Structured Scripts cites this paper.

FoleyDirector: Fine-Grained Temporal Steering for Video-to-Audio Generation via Structured Scripts AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:39:50.480308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 01d80f55-4586-43b5-b625-b49aebed3ee0 · inbound

RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details cites this paper.

RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T23:20:54.236052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.