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Paper Citation Record · LEDGER

EarthSynth: Generating Informative Earth Observation with Diffusion Models

As of 18 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 6 inbound Pith citation observations for arXiv:2505.12108.

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

pith.paper-citation-record.v1
2505.12108 v2

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

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measured 77 of 77 standing notices

One-hop event checks from named stored sources.

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measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:28:21.278631Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T11:04:08.269882Z

Reference resolution

71 of 71 outbound references displayed

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External citation measurements

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Outbound references

Observation 724ee6a3-5dd7-4f7c-a6c5-0549af2fa553 · outbound

This paper cites Research progress on few-shot learning for remote sensing image interpretation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Research progress on few-shot learning for remote sensing image interpretation,

Reference 1

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Observation 82b3ea45-10b7-4695-81b6-2f95aa8cef78 · outbound

This paper cites Addressing class imbalance in remote sensing using deep learning approaches: a systematic literature review,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Addressing class imbalance in remote sensing using deep learning approaches: a systematic literature review,

Reference 2

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Observation b0b1432e-7d12-471c-8fd9-bd813b3ec002 · outbound

This paper cites Data Augmentation Generative Adversarial Networks.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Data Augmentation Generative Adversarial Networks

Reference 3

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Observation 0b4f4550-f97b-4214-bc61-886138d89743 · outbound

This paper cites High-resolution image synthesis with latent diffusion models,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models High-resolution image synthesis with latent diffusion models,

Reference 4

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Observation b95c2785-4cae-46c6-b0f6-61ae980473e3 · outbound

This paper cites A comprehensive survey for generative data augmentation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models A comprehensive survey for generative data augmentation,

Reference 5

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Observation 01a133c2-618d-45e6-b2dd-eac8978168ef · outbound

This paper cites Is synthetic data from generative models ready for image recognition?.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Is synthetic data from generative models ready for image recognition?

Reference 6

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Observation ab91bfcf-44be-4e43-af46-a4a070669d86 · outbound

This paper cites Effective Data Augmentation With Diffusion Models.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Effective Data Augmentation With Diffusion Models

Reference 7

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Observation 762eb648-1dc2-45ea-9a63-8c6df674248e · outbound

This paper cites Learning disentangled identifiers for action-customized text-to-image generation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Learning disentangled identifiers for action-customized text-to-image generation,

Reference 8

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Observation 90c89c3c-3095-46e4-9210-6648ff28b19c · outbound

This paper cites Self-Improving Diffusion Models with Synthetic Data.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Self-Improving Diffusion Models with Synthetic Data

Reference 9

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Observation 33809995-48b4-484b-948f-348fa446d5ac · outbound

This paper cites Self-consuming generative models go MAD,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Self-consuming generative models go MAD,

Reference 10

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Observation 8085d484-c918-4b17-bc79-6644c0f6420a · outbound

This paper cites Diversify your vision datasets with automatic diffusion-based augmentation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Diversify your vision datasets with automatic diffusion-based augmentation,

Reference 11

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Observation 4b69f228-10e1-40cd-a8bb-e2db8f31ee95 · outbound

This paper cites Txt2img-mhn: Remote sensing image generation from text using modern hopfield networks,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Txt2img-mhn: Remote sensing image generation from text using modern hopfield networks,

Reference 12

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Observation e945448a-a341-43f2-86de-ef702b5ce5ec · outbound

This paper cites Improved techniques for training gans,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Improved techniques for training gans,

Reference 13

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Observation 63ed781f-c8ca-4c3d-9334-cf829e300833 · outbound

This paper cites DiffusionSat: A Generative Foundation Model for Satellite Imagery.

EarthSynth: Generating Informative Earth Observation with Diffusion Models DiffusionSat: A Generative Foundation Model for Satellite Imagery

Reference 14

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Observation 1938bec9-92bb-4370-bc43-33378b89cc4e · outbound

This paper cites Crs-diff: Controllable remote sensing image generation with diffusion model,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Crs-diff: Controllable remote sensing image generation with diffusion model,

Reference 15

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Observation 2c56bf36-b081-4df8-b37b-2d770fcec4b6 · outbound

This paper cites Satsynth: Augmenting image- mask pairs through diffusion models for aerial semantic segmentation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Satsynth: Augmenting image- mask pairs through diffusion models for aerial semantic segmentation,

Reference 16

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Observation 238cfac1-1244-4d88-abc9-7c34c97eec28 · outbound

This paper cites AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation.

EarthSynth: Generating Informative Earth Observation with Diffusion Models AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation

Reference 17

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Observation 0201323c-df5d-4737-8981-829f6b376359 · outbound

This paper cites Mmo-ig: Multi-class and multi-scale object image generation for remote sensing,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Mmo-ig: Multi-class and multi-scale object image generation for remote sensing,

Reference 18

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Observation 111a2922-5bfe-45a3-a692-bd8512b9f072 · outbound

This paper cites Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model

Reference 19

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Observation e5dc2799-cc54-4714-bdd1-bd954ff4a091 · outbound

This paper cites Diffusion models meet remote sensing: Principles, methods, and perspectives,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Diffusion models meet remote sensing: Principles, methods, and perspectives,

Reference 20

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Observation 79e41d3a-469a-473c-baff-a200438ffca5 · outbound

This paper cites Dual-diffusion: Dual conditional denoising diffusion probabilistic models for blind super-resolution reconstruction in rsis,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Dual-diffusion: Dual conditional denoising diffusion probabilistic models for blind super-resolution reconstruction in rsis,

Reference 21

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Observation e8dce29c-5916-4232-96df-a136835eae55 · outbound

This paper cites A multiscale generalized shrinkage threshold network for image blind deblurring in remote sensing,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models A multiscale generalized shrinkage threshold network for image blind deblurring in remote sensing,

Reference 22

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Observation bc2cf05b-401a-4d73-9a35-f4745c4c33a8 · outbound

This paper cites Diffusion models for spatio-temporal-spectral fusion of homogeneous gaofen-1 satellite platforms,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Diffusion models for spatio-temporal-spectral fusion of homogeneous gaofen-1 satellite platforms,

Reference 23

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Observation 878a271a-25fc-40d7-a4d6-fc2f1d58d728 · outbound

This paper cites Hyperspectral and panchromatic images fusion based on the dual conditional diffusion models,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Hyperspectral and panchromatic images fusion based on the dual conditional diffusion models,

Reference 24

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Observation afdc7f83-9d43-4af9-b70f-d75d9686e98f · outbound

This paper cites DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection.

EarthSynth: Generating Informative Earth Observation with Diffusion Models DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection

Reference 25

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Observation 4327f1aa-29e4-4508-bf87-26da9434c0ef · outbound

This paper cites Pred- iff: Precipitation nowcasting with latent diffusion models,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Pred- iff: Precipitation nowcasting with latent diffusion models,

Reference 26

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Observation 5fe11f99-761b-4eef-87f5-16e74fe558a4 · outbound

This paper cites Geosynth: Contextually-aware high-resolution satellite image synthesis,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Geosynth: Contextually-aware high-resolution satellite image synthesis,

Reference 27

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Observation 738bcaf6-b2ef-43df-87ee-3b5c470f8b0f · outbound

This paper cites Advancing controllable diffusion model for few-shot object detection in optical remote sensing imagery,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Advancing controllable diffusion model for few-shot object detection in optical remote sensing imagery,

Reference 28

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Observation 604f17e9-a420-4ac3-b510-40a659b21340 · outbound

This paper cites Controllable generative knowledge driven few-shot object detection from optical remote sensing imagery,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Controllable generative knowledge driven few-shot object detection from optical remote sensing imagery,

Reference 29

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Observation c4723f77-dee0-4d4d-8200-63e58e3d77d5 · outbound

This paper cites Control Copy-Paste: Controllable Diffusion-Based Augmentation Method for Remote Sensing Few-Shot Object Detection.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Control Copy-Paste: Controllable Diffusion-Based Augmentation Method for Remote Sensing Few-Shot Object Detection

Reference 30

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Observation 440ab313-f529-42dd-931b-3507ebfedca8 · outbound

This paper cites Domain-rag: Retrieval-guided compositional image generation for cross-domain few- shot object detection,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Domain-rag: Retrieval-guided compositional image generation for cross-domain few- shot object detection,

Reference 31

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Observation 9f8c8a53-32ed-4419-8348-118e0d2ad9d9 · outbound

This paper cites Styleadv: Meta style adversarial training for cross-domain few-shot learning,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Styleadv: Meta style adversarial training for cross-domain few-shot learning,

Reference 32

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Observation fc8238df-ceff-4921-9d45-7ae49bb0195b · outbound

This paper cites Ntire 2025 challenge on cross-domain few-shot object detection: methods and results,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Ntire 2025 challenge on cross-domain few-shot object detection: methods and results,

Reference 33

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Observation 3294a439-ec78-43f8-90da-ee7cd21ff144 · outbound

This paper cites Out-of-domain robustness via targeted augmentations,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Out-of-domain robustness via targeted augmentations,

Reference 34

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Source-reported events for the cited work

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Observation 00f96741-a07d-4b76-899b-a91c2501c55d · outbound

This paper cites Reducing semantic confusion: Scene-aware aggregation network for remote sensing cross-modal retrieval,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Reducing semantic confusion: Scene-aware aggregation network for remote sensing cross-modal retrieval,

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Observation bd3e59bb-4850-4ae4-b5e5-7ba334bd57cb · outbound

This paper cites A prior instruction representation framework for remote sensing image-text retrieval,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models A prior instruction representation framework for remote sensing image-text retrieval,

Reference 36

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Observation ebb86f6a-e6ef-4ee3-a1f3-6f5eab40f6e0 · outbound

This paper cites PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval.

EarthSynth: Generating Informative Earth Observation with Diffusion Models PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval

Reference 37

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Observation bc9517a2-9851-43a8-8de3-23b96c13ab9e · outbound

This paper cites Simple copy-paste is a strong data augmentation method for instance segmentation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Simple copy-paste is a strong data augmentation method for instance segmentation,

Reference 38

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Observation ec367884-14c4-4a42-95cb-4bd707d61b3a · outbound

This paper cites Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,

Reference 39

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Observation da78d6a6-f2ce-44e0-8348-ecd61cee3da2 · outbound

This paper cites Direction-oriented visual–semantic embedding model for remote sensing image–text retrieval,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Direction-oriented visual–semantic embedding model for remote sensing image–text retrieval,

Reference 40

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Observation 84588155-3830-4e30-8709-4e1d8fc125b0 · outbound

This paper cites On the importance of gradients for detecting distributional shifts in the wild,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models On the importance of gradients for detecting distributional shifts in the wild,

Reference 41

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Observation 02e49a5f-5a92-4c56-b4ad-008b3d41b9b0 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models U-net: Convolutional networks for biomedical image segmentation,

Reference 42

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Observation a98f9d6f-2ec5-41b1-8680-a74474165372 · outbound

This paper cites Adding conditional control to text-to-image diffu- sion models,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Adding conditional control to text-to-image diffu- sion models,

Reference 43

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Observation 914c398b-276e-471f-b3ac-29c28ef8ee93 · outbound

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

EarthSynth: Generating Informative Earth Observation with Diffusion Models Instancediffusion: Instance- level control for image generation,

Reference 44

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Observation d12ce751-0ea1-4979-9de8-a195ffd6fb7e · outbound

This paper cites Automatic segmentation and fitting of image edge contours based on douglas algorithm,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Automatic segmentation and fitting of image edge contours based on douglas algorithm,

Reference 45

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Observation ffff67cb-b09c-4afe-9b14-b169b324ef7b · outbound

This paper cites Openearthmap: A benchmark dataset for global high-resolution land cover mapping,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Openearthmap: A benchmark dataset for global high-resolution land cover mapping,

Reference 46

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Observation 25dffc28-09c8-428d-be03-b99b8ccb5394 · outbound

This paper cites LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation.

EarthSynth: Generating Informative Earth Observation with Diffusion Models LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Reference 47

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Observation 4513fffc-96cc-4c24-9e90-29ca0a217985 · outbound

This paper cites Deepglobe 2018: A challenge to parse the earth through satellite images,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Deepglobe 2018: A challenge to parse the earth through satellite images,

Reference 48

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Observation 3c3c47d9-8e79-48cd-97a9-ef6736412c3a · outbound

This paper cites Samrs: Scaling-up remote sensing segmentation dataset with segment anything model,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Samrs: Scaling-up remote sensing segmentation dataset with segment anything model,

Reference 49

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Observation 30160d8c-a67e-475a-82c1-ff4208dc89ce · outbound

This paper cites Locate anything on earth: Advancing open-vocabulary object detection for remote sensing community,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Locate anything on earth: Advancing open-vocabulary object detection for remote sensing community,

Reference 50

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Observation 50159442-87d7-4676-b0fb-a55c1ed24f09 · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Remote sensing image scene classification: Benchmark and state of the art,

Reference 51

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Observation 3f163f38-1ba2-47de-9948-6881766ca4c2 · outbound

This paper cites Object detection in 20 years: A survey,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Object detection in 20 years: A survey,

Reference 52

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Observation 2d250212-5a6f-4657-8120-e219427d05e7 · outbound

This paper cites A review of semantic segmentation using deep neural networks,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models A review of semantic segmentation using deep neural networks,

Reference 53

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Observation 91d4f36c-6236-41ae-a9bb-775e2ed5e508 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Learning transferable visual models from natural language supervi- sion,

Reference 54

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Observation 6c742172-67c8-4f1a-a971-e4a63c8b34f1 · outbound

This paper cites Detecting twenty-thousand classes using image-level supervision,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Detecting twenty-thousand classes using image-level supervision,

Reference 55

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Observation 5bc0b428-493d-439f-9226-d1d68d011e7c · outbound

This paper cites Detect Everything with Few Examples.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Detect Everything with Few Examples

Reference 56

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Observation ad508675-862e-471d-b812-fdce2872e0b6 · outbound

This paper cites Cross-domain few-shot object detection via enhanced open-set object detector,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Cross-domain few-shot object detection via enhanced open-set object detector,

Reference 57

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Observation 6882c257-9b19-4f86-8c37-15d8923a5f31 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Grounding dino: Marrying dino with grounded pre-training for open-set object detection,

Reference 58

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Observation 1740c8b1-bbc3-4c06-acd8-878c172f9a0d · outbound

This paper cites Enhance then search: An augmentation-search strategy with foundation models for cross-domain few-shot object detec- tion,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Enhance then search: An augmentation-search strategy with foundation models for cross-domain few-shot object detec- tion,

Reference 59

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Observation a275b397-3c41-442e-89e3-363381db6e0f · outbound

This paper cites Exploring models and data for remote sensing image caption generation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Exploring models and data for remote sensing image caption generation,

Reference 60

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Observation 9a7ae2b5-5b90-47fa-845a-e12d140f44cb · outbound

This paper cites Towards open-vocabulary remote sensing image semantic segmentation,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Towards open-vocabulary remote sensing image semantic segmentation,

Reference 61

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Observation f73260c2-cfc4-47dd-b8ab-4029abfcc607 · outbound

This paper cites Object detection in optical remote sensing images: A survey and a new benchmark,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Object detection in optical remote sensing images: A survey and a new benchmark,

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This paper cites Dota: A large-scale dataset for object detection in aerial images,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Dota: A large-scale dataset for object detection in aerial images,

Reference 63

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Observation 799c120d-a49c-4329-ab39-6e37e10bd211 · outbound

This paper cites 2d semantic labeling potsdam dataset.

EarthSynth: Generating Informative Earth Observation with Diffusion Models 2d semantic labeling potsdam dataset

Reference 64

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Observation a3713d0b-7756-4e8d-bc1d-ad23086da4b6 · outbound

This paper cites Floodnet: A high resolution aerial imagery dataset for post flood scene understanding,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Floodnet: A high resolution aerial imagery dataset for post flood scene understanding,

Reference 65

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Observation d012933e-8344-4679-ac1c-dc98c0f024bd · outbound

This paper cites Flair: a country-scale land cover semantic segmentation dataset from multi-source optical imagery,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Flair: a country-scale land cover semantic segmentation dataset from multi-source optical imagery,

Reference 66

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Observation 337b50f9-64c2-48b8-883f-0a2f3c0025b8 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 67

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Observation 90d8bca8-bd31-42dd-a27d-65f38ed0da7d · outbound

This paper cites Visualizing data using t-sne.,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Visualizing data using t-sne.,

Reference 68

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Observation a478eef4-234f-434d-b93f-41a38bed136a · outbound

This paper cites Segment anything,.

EarthSynth: Generating Informative Earth Observation with Diffusion Models Segment anything,

Reference 69

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Observation 5c5f24c9-cabf-4c2f-b0da-cd9a79b8dc2d · outbound

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EarthSynth: Generating Informative Earth Observation with Diffusion Models Unresolved cited work

Reference 70

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EarthSynth: Generating Informative Earth Observation with Diffusion Models #$!: ℒ%!

Reference 71

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Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation cites this paper.

Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation EarthSynth: Generating Informative Earth Observation with Diffusion Models

Reference 21

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Control Copy-Paste: Controllable Diffusion-Based Augmentation Method for Remote Sensing Few-Shot Object Detection cites this paper.

Control Copy-Paste: Controllable Diffusion-Based Augmentation Method for Remote Sensing Few-Shot Object Detection EarthSynth: Generating Informative Earth Observation with Diffusion Models

Reference 15

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TerraDiT: Point-Conditioned Diffusion Transformer for Satellite Image Synthesis cites this paper.

TerraDiT: Point-Conditioned Diffusion Transformer for Satellite Image Synthesis EarthSynth: Generating Informative Earth Observation with Diffusion Models

Reference 39

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RSEdit: Text-Guided Image Editing for Remote Sensing cites this paper.

RSEdit: Text-Guided Image Editing for Remote Sensing EarthSynth: Generating Informative Earth Observation with Diffusion Models

Reference 17

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From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation cites this paper.

From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation EarthSynth: Generating Informative Earth Observation with Diffusion Models

Reference 34

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Observation 8e58a8b3-2300-49d8-8130-eec1c3825a2e · inbound

From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation cites this paper.

From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation EarthSynth: Generating Informative Earth Observation with Diffusion Models

Reference 34

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