Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision | Paper Citation Record

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This paper cites Qwen2.5-VL Technical Report.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Qwen2.5-VL Technical Report

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This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 2

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This paper cites GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation

Reference 3

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This paper cites YOLO-World: Real-Time Open-V ocabulary Object Detection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision YOLO-World: Real-Time Open-V ocabulary Object Detection

Reference 4

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This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 5

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This paper cites Diffusion Models Beat GANs on Image Synthesis.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Diffusion Models Beat GANs on Image Synthesis

Reference 6

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This paper cites LaMI-DETR: Open-V ocabulary Detection with Lan- guage Model Instruction.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision LaMI-DETR: Open-V ocabulary Detection with Lan- guage Model Instruction

Reference 7

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

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Diversify your vision datasets with automatic diffusion-based augmentation

Reference 8

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Observation e32f9746-b143-49e6-8799-ddeb51cd2e7d · outbound

This paper cites Everingham, L.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Everingham, L

Reference 9

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Observation 0331fb67-be02-406d-809b-38b5c6fb01a2 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 10

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Observation 31922317-245d-47b8-bd32-a4fdec52ad6c · outbound

This paper cites Deep Residual Learning for Image Recognition.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Deep Residual Learning for Image Recognition

Reference 11

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Observation d0eac44e-ced4-445c-90a3-7c8212d7530b · outbound

This paper cites Denoising Dif- fusion Probabilistic Models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Denoising Dif- fusion Probabilistic Models

Reference 12

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Observation ebe1ef8d-b47e-4eba-8297-21d74241c00c · outbound

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

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Cross-domain weakly-supervised object de- tection through progressive domain adaptation

Reference 13

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This paper cites DGIn- Style: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision DGIn- Style: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control

Reference 14

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Observation 0606f485-6f53-4f52-a172-e4051db67f9d · outbound

This paper cites Yolov5 by ultralytics, 2020.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Yolov5 by ultralytics, 2020

Reference 15

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Observation ad77b01c-e748-4a41-bf50-89e66c1eceff · outbound

This paper cites Align and Distill: Unifying and Improving Domain Adaptive Object Detection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Align and Distill: Unifying and Improving Domain Adaptive Object Detection

Reference 16

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This paper cites Lobell, and Ste- fano Ermon.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Lobell, and Ste- fano Ermon

Reference 17

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Observation 931d8563-bd63-4ad5-a0f0-73f635f5dd24 · outbound

This paper cites Text-Image Alignment for Diffusion-Based Perception.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Text-Image Alignment for Diffusion-Based Perception

Reference 18

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Observation adcec43b-850a-4160-8ef7-45bb92d51d5e · outbound

This paper cites Scaling novel object detection with weakly su- pervised detection transformers.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Scaling novel object detection with weakly su- pervised detection transformers

Reference 19

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Observation 111b1b53-8c21-4586-8955-a220c8f299ac · outbound

This paper cites Markov chains and mixing times.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Markov chains and mixing times

Reference 20

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Observation 094461ae-5a61-4872-a98d-d88c5ccf3d57 · outbound

This paper cites Your diffusion model is secretly a zero-shot classifier.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Your diffusion model is secretly a zero-shot classifier

Reference 21

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Observation 1161c314-d525-43f6-9961-c2599bbc37c7 · outbound

This paper cites BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 22

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Observation 91f83bc6-da8e-4d64-9377-09647fa2817b · outbound

This paper cites Grounded language-image pre-training.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Grounded language-image pre-training

Reference 23

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Observation 5fad4896-c32b-419a-8663-dbdf65b94cc3 · outbound

This paper cites Sigma: Semantic- complete graph matching for domain adaptive object detec- tion.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Sigma: Semantic- complete graph matching for domain adaptive object detec- tion

Reference 24

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Observation 3fbbe569-1c99-4705-b854-ae5dc08225b1 · outbound

This paper cites Exploring plain vision transformer backbones for object de- tection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Exploring plain vision transformer backbones for object de- tection

Reference 25

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Observation e81e864a-885e-4f53-8ac7-54824abfa87c · outbound

This paper cites Gligen: Open-set grounded text-to-image generation.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Gligen: Open-set grounded text-to-image generation

Reference 26

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Observation 2637c02f-633e-4d2d-9f32-f145e8c60850 · outbound

This paper cites Cross-Domain Adaptive Teacher for Object Detection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Cross-Domain Adaptive Teacher for Object Detection

Reference 27

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Observation 047649d1-eff4-4fa0-b81a-4bbfddf5df56 · outbound

This paper cites Microsoft coco: Common objects in context.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Microsoft coco: Common objects in context

Reference 28

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This paper cites Selwyn 0.125m Urban Aerial Photos (2012-2013).

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Selwyn 0.125m Urban Aerial Photos (2012-2013)

Reference 29

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This paper cites LLaV A-NeXT: Im- proved reasoning, OCR, and world knowledge, 2024.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision LLaV A-NeXT: Im- proved reasoning, OCR, and world knowledge, 2024

Reference 30

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Observation f09e2c4c-e179-4337-a9aa-6bf3f1623fb1 · outbound

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

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Grounding dino: Marrying DINO with Grounded Pre-training for Open-Set Object Detection

Reference 31

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Swin transformer: Hierarchical vision transformer using shifted windows

Reference 32

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This paper cites Simple open-vocabulary object detection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Simple open-vocabulary object detection

Reference 33

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Observation 52ab58cf-c4ad-4abb-9d9b-d143accc20cf · outbound

This paper cites Scaling Open-V ocabulary Object Detection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Scaling Open-V ocabulary Object Detection

Reference 34

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Observation 46a5fd5b-20ff-4233-a2e0-edc0eb42ce6e · outbound

This paper cites Dataset Diffusion: Diffusion-based Synthetic Data Generation for Pixel-Level Semantic Segmentation.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Dataset Diffusion: Diffusion-based Synthetic Data Generation for Pixel-Level Semantic Segmentation

Reference 35

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Observation 0c96df87-6bb0-4f5b-918f-298b6bae5b54 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Improved denoising diffusion probabilistic models

Reference 36

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This paper cites AttnDreamBooth: To- wards Text-Aligned Personalized Text-to-Image Generation.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision AttnDreamBooth: To- wards Text-Aligned Personalized Text-to-Image Generation

Reference 37

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Observation 3a30f20e-21a5-459f-a0cb-75565941901e · outbound

This paper cites Shape-Guided Diffusion With Inside-Outside Atten- tion.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Shape-Guided Diffusion With Inside-Outside Atten- tion

Reference 38

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Observation d5d233cc-25f8-4d92-a3ad-7d784a808b4a · outbound

This paper cites One-Step Image Translation with Text-to-Image Models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision One-Step Image Translation with Text-to-Image Models

Reference 39

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This paper cites Ground- ing multimodal large language models to the world.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Ground- ing multimodal large language models to the world

Reference 40

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Observation 46d5f111-5bfb-4870-8faa-832c9bf39f85 · outbound

This paper cites SDXL: Improving latent diffusion models for high-resolution image synthesis.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision SDXL: Improving latent diffusion models for high-resolution image synthesis

Reference 41

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Observation 86573e28-fd19-43b2-894b-ff7f59a2bea6 · outbound

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

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Learning transferable visual models from natural language supervi- sion

Reference 42

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Observation f67f3f0e-8bcc-4e78-9eda-da73f30166c9 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 43

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This paper cites Real-Time Flying Object Detection with YOLOv8.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Real-Time Flying Object Detection with YOLOv8

Reference 44

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This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 45

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Observation 2affbe92-25ef-4bd6-beba-e0563e2e32e6 · outbound

This paper cites High-Resolution Image Synthesis With Latent Diffusion Models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision High-Resolution Image Synthesis With Latent Diffusion Models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:07.033282Z

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Observation 52b30694-73fb-4b4a-9c7a-e6b7b608b804 · outbound

This paper cites DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

Reference 47

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Observation b72d4fd9-9e39-4279-abff-52b0c8f26224 · outbound

This paper cites Imagenet large scale visual recognition challenge.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Imagenet large scale visual recognition challenge

Reference 48

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Observation 236c38e8-6333-4240-ac08-cbc20d5bdd90 · outbound

This paper cites LAION-5b: An open large-scale dataset for train- ing next generation image-text models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision LAION-5b: An open large-scale dataset for train- ing next generation image-text models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.984016Z

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Observation f32e1a82-cf4b-4a88-8870-4d1dba098ff0 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Deep unsupervised learning using nonequilibrium thermodynamics

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.968136Z

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Observation 766e2f66-1e48-40fa-9a68-ed1bc2351c62 · outbound

This paper cites Satdiffmoe: A mixture of estimation method for satellite image super- resolution with latent diffusion models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Satdiffmoe: A mixture of estimation method for satellite image super- resolution with latent diffusion models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.952603Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 029b2efb-3b72-4356-9b2a-68c07e280bc7 · outbound

This paper cites Denois- ing diffusion implicit models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Denois- ing diffusion implicit models

Reference 52

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Observation 09521599-aca2-492f-8336-ca075096618b · outbound

This paper cites Multiple Instance Detection Network With Online Instance Classifier Refinement.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Multiple Instance Detection Network With Online Instance Classifier Refinement

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.925723Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d647360-6392-47e6-98d7-61fd0aa2b2fb · outbound

This paper cites What the DAAM: Interpreting Stable Dif- fusion Using Cross Attention.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision What the DAAM: Interpreting Stable Dif- fusion Using Cross Attention

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.909759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d0fab6f5-4505-4f15-92a5-2f2626b393d6 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Gemini: A Family of Highly Capable Multimodal Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T13:12:06.337222Z

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Observation 34c6e50b-2bf9-4d95-ba97-40f47ee9b28b · outbound

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

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T13:12:06.341602Z

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Observation e584832c-4391-4219-a558-f9af6aefb304 · outbound

This paper cites Utah High Resolution Orthophotography (HRO) 2012 Images.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Utah High Resolution Orthophotography (HRO) 2012 Images

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.894881Z

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Observation 183e5063-45fe-4159-9850-acb2f531f086 · outbound

This paper cites Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T13:12:06.350579Z

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Observation 402e3dcc-9078-49c0-b9bd-69c59fa3b1cc · outbound

This paper cites Domain Gap Embeddings for Genera- tive Dataset Augmentation.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Domain Gap Embeddings for Genera- tive Dataset Augmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.878986Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b66df98b-3a53-450c-8461-09b3ca575338 · outbound

This paper cites DatasetDM: Synthesizing Data with Perception An- notations Using Diffusion Models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision DatasetDM: Synthesizing Data with Perception An- notations Using Diffusion Models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.863546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b0993f68-6bec-440c-836e-9bb625598252 · outbound

This paper cites DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.848127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1bf6e65d-0a29-4254-97da-f2616e0175ee · outbound

This paper cites SOEDiff: Efficient Distillation for Small Object Editing.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision SOEDiff: Efficient Distillation for Small Object Editing

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.832307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2ce474e9-4474-42b6-a37e-7471d9232c5d · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T13:12:06.371800Z

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Observation 31fff862-0df7-4eb1-a773-09c7fd1f741b · outbound

This paper cites DOTA: A Large-Scale Dataset for Object Detec- tion in Aerial Images.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision DOTA: A Large-Scale Dataset for Object Detec- tion in Aerial Images

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.817802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 221442a2-ca7f-4dee-94ff-1643a50d9b61 · outbound

This paper cites CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.802644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation de6f0c9a-b6f9-4018-af05-1cd588b3ca8e · outbound

This paper cites H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object De- tection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object De- tection

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.787531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 35e0d888-7848-4f76-9839-a6e99599665f · outbound

This paper cites A survey on multimodal large language models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision A survey on multimodal large language models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T13:12:06.389683Z

Source-reported events for the cited work

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Observation 51376e8a-3a56-4da4-b31c-595c0fb87875 · outbound

This paper cites Sigmoid Loss for Language Image Pre- training.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Sigmoid Loss for Language Image Pre- training

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.759476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 813b8895-4a18-4262-8bf0-d1063f88e34f · outbound

This paper cites Adding Conditional Control to Text-to-Image Diffusion Models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Adding Conditional Control to Text-to-Image Diffusion Models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.744836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 487d48a6-1d60-4eac-8300-5c02e46e937c · outbound

This paper cites DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:12:06.500165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 763406e4-df34-474b-b3f5-c4b9a061e043 · outbound

This paper cites Task-Specific Inconsistency Alignment for Domain Adaptive Object Detection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Task-Specific Inconsistency Alignment for Domain Adaptive Object Detection

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.729606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 882730d5-6ede-47b2-9511-4b58194dc926 · outbound

This paper cites Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T13:12:06.410938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40e9ec4c-3c0b-402b-82dd-3cf554a1abc3 · outbound

This paper cites Boosting weakly supervised object detection with progres- sive knowledge transfer.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision Boosting weakly supervised object detection with progres- sive knowledge transfer

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.713415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 141f80d2-b9c6-4bb7-9d07-9e5bd82878b9 · outbound

This paper cites SSDA-YOLO: Semi-supervised domain adaptive YOLO for cross-domain object detection.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision SSDA-YOLO: Semi-supervised domain adaptive YOLO for cross-domain object detection

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:12:06.696988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b5cb94b7-3262-4a5a-8e90-e10ee9556502 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T13:12:06.423775Z

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