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Paper Citation Record · LEDGER

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

As of 16 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2507.20976.

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

pith.paper-citation-record.v1
2507.20976 v1

Coverage vector

measured 75 of 75 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:12:06.423775Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

75 of 75 outbound references displayed

  • verified exact1
  • verified fuzzy55
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 18393383-cef4-485d-a9df-b09a2a4eadbf · outbound

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

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0b151a60-532d-47c7-828a-5a29a70de32c · outbound

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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no resolver link, observed 2026-08-06T13:12:06.110239Z

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Unavailable: canonical work link unavailable.

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Observation e3c5c90b-0e49-4760-88b2-3ad5e55b67c3 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation b998e982-5999-4998-9339-5cddf4cadfbb · outbound

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 31aeaeea-1a6b-4904-a7f5-6849ad89cfae · outbound

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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no resolver link, observed 2026-08-06T13:12:06.124040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d1e4c220-9d42-4925-b084-c4d4bf12c7fe · outbound

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 3086ae69-efcd-4fa1-9f60-b22777274d8d · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.133883Z digest=sha256:22d1dd8eee3018436365e1997207ecdf7ef2ab3c12616c168e817898953da524

Observation e34d236d-a286-4258-a5b0-033a7b660f20 · outbound

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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raw_fallback, observed 2026-08-06T13:12:07.525296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.150876Z digest=sha256:153378dbe4b3a8b15bcc8cceaad2fb02d31ebab893a6c539becffc6aaa227a09

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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raw_fallback, observed 2026-08-06T13:12:07.466548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.155441Z digest=sha256:448b7989233fce9fa264bdf0ad3df46fb1e238baaacabeeda2c7a341c87525dd

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 9646fa4e-9fed-4163-98d3-e58c9bedecaf · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.436204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.167179Z digest=sha256:84ce94e5bed243b9012d2434bf6f656776c9488bbb5e6da51b39a70acb97d682

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.171255Z digest=sha256:ef659162ea5278aa88ab6291f89d894067f06c0ac11e70258959102912110e11

Observation 775f1a1f-0295-4130-aff4-3b997ab5fc5c · outbound

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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raw_fallback, observed 2026-08-06T13:12:07.404777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.175554Z digest=sha256:ffb382bdb0250629e16abab02acc2894481d7ae8d9aeb5c07ab7895a015e75dc

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

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.179902Z digest=sha256:75f21be65138d72dbc13e2d4e92a9ee44820694d59bc5480c148c84c48f74c7e

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.184117Z digest=sha256:2442754aa17c7adea3f42436c48a5b12b19b7c737146d4401d93110b47e86c6b

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.192006Z digest=sha256:9721fddd7fe5b256627e746e608a3fd2c02995af8da1c5fd99d8577f57190ee7

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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raw_fallback, observed 2026-08-06T13:12:07.334657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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raw_fallback, observed 2026-08-06T13:12:07.303228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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raw_fallback, observed 2026-08-06T13:12:07.274452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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raw_fallback, observed 2026-08-06T13:12:07.258738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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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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no resolver link, observed 2026-08-06T13:12:06.221258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.221258Z digest=sha256:a7519a76897f746848a608aee2529777104d0f3aa139790bc2e56af61cecfc2b

Observation 3e023c07-f0ee-416a-8808-49aed718d620 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.234514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation fdcea3e7-18b4-40cd-8774-e14e69af4854 · outbound

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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raw_fallback, observed 2026-08-06T13:12:07.219407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.229401Z digest=sha256:f47615aaaf300c53148c3f3cd86809d3aeff1de3b9a5db0fda747605305158e9

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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raw_fallback, observed 2026-08-06T13:12:07.204629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.234241Z digest=sha256:4136941db3a08d3832ec1034c64d99805876fd060b3cff13e523fe8145376564

Observation 01912d39-f302-47f9-9490-fc40af4f9721 · outbound

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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unresolved
no resolver link, observed 2026-08-06T13:12:06.238106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.238106Z digest=sha256:a2ee63fbcbd7f00cf9db2404497d92d255905bc2d24128ecb4a8f2b5b9fc67d3

Observation 406786c6-3107-4a36-8471-25e1c0341ec5 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.178961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.242131Z digest=sha256:222c3d71270f292a044e56d5d96adcbc04fceffc3ac8a329011a4a5c667bbe92

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.164477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.246083Z digest=sha256:8f761c2591d1c704435ea7909d64a97600716dd74dd8f3483b814c78ec1a590a

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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raw_fallback, observed 2026-08-06T13:12:07.149897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.250813Z digest=sha256:7460f9dcb8095586e3718d97726fa332d6f10e2875aa2d95dcea7d350ccdee7b

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

Resolution
unresolved
no resolver link, observed 2026-08-06T13:12:06.254740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.254740Z digest=sha256:a055e82cad49fedf0a745fdaf738e9c136412d87b583ab25778b8459a8038d00

Observation fe11c488-3700-4d72-88a3-4c4a7364f09c · outbound

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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verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.125005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.258920Z digest=sha256:bedf069991b65f0ad5c9de91e62c6d12f78acb13646e15f1f24f1d0481f1f42f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.110158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.262883Z digest=sha256:83e2dbb5af8859f4fda4b194f0ba06b7e8a788a2a75ca66eabebf21e56c60bc6

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

Resolution
unresolved
no resolver link, observed 2026-08-06T13:12:06.266989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.266989Z digest=sha256:d6b12ae522d01bc2e627b49f37722a5df4e11243f5216b6adf311cbd3dacd536

Observation 1a978367-a176-42f6-84f5-e4485fd075da · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.095295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.271748Z digest=sha256:691e0292fadd9e829bd96a5babb1e5d510b72250ccc63ce44118e0d9228747f2

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.080123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.276086Z digest=sha256:7638c5c0c05be02b0296f8ea877667af56f0d01bec385f98cce3c0134ba82040

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.065364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.280256Z digest=sha256:8606fa974941f99003758d3531362f18abfef355100afaec1f0ecbb665794c11

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

Resolution
unresolved
no resolver link, observed 2026-08-06T13:12:06.284887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.284887Z digest=sha256:c0b7a5e611e47d3916721864a6afc47a6ec5b16a5d6bb978222d18eb4298feb7

Observation fce8af78-2943-4918-90c4-2e93ec202f85 · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-06T13:12:06.289673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.289673Z digest=sha256:4a7e0e97bd7348f24ebf9ebc1ba60daf45f203123312510188cef932bdee49f6

Observation 828d0ddb-5d3b-4db5-8789-5e1b4a312b54 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.049589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.293923Z digest=sha256:de39a015b60d8e4e56387a71f86347d16ff8f27d6c54778c43aa648ff2aaa8fb

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.297985Z digest=sha256:8a71ce161a2a0d6dfb47e4bb8247e6578be98af09c653fe5c179cde5eb5f3679

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:07.015643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.302032Z digest=sha256:aa9b6ac401c12e9fd81a704a5fb0d7bf112420049780a8d4c46831ce45939509

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:12:06.999800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.306079Z digest=sha256:6743ab4f0696c75af106ae084a1c891295c698eed635ded087b2541cd2511275

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.310626Z digest=sha256:846eb62938d2c410e1c7c79c3954433c2d8edac5cad6f5a19f1ba650829f799e

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.314819Z digest=sha256:fb2d34fce37b62489bac63ac644e05fac9fa20c84be4ccaf0fa32a2bd8b1b22e

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.319253Z digest=sha256:cd0c2629e9700e06f70426b461602f0b69d98c1bd27597dd09676fe471ff29d9

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

Resolution
unresolved
no resolver link, observed 2026-08-06T13:12:06.323592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.323592Z digest=sha256:dc19fb47cb38a12e64ed94113c7d9f1bd4c5bcbc96cdaadbaca9cc4b05293ee6

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.327896Z digest=sha256:f28d080964022bdd45e591a79938b971f1e1f9ee067e80e967ba51558a40dd0b

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.332631Z digest=sha256:23d1eb08e0c1e86e6deecdd6e8eadd6dbcee14bae5b8e58153367d1deb6f6f30

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.337222Z digest=sha256:3156134786ece445feb7c3b235397aa0028f1c5b40a43c69c0b6963f648d796e

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.341602Z digest=sha256:0934ba3004537998184d47236f192eb3d31b1ff31a938be243130ee07e9a749e

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.345963Z digest=sha256:cf21fa759f4559085f41463d01bbbd9ecdcc8eed2887c9b463b3f46485870082

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.350579Z digest=sha256:cfd8b55a4df6e823bc9477579ee2085f9a7d39ca9de5afec56255350be2dc398

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.355285Z digest=sha256:1d7ae7c4f4514a06958e25d4f4058df5475ec84894a82bce6863a3359fe4ec2c

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.359574Z digest=sha256:cef5980528045cb1747cd636d52e3d116d161ce73b1920462ec0e7a97d911dba

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.363767Z digest=sha256:5ebece1498d8bdd27909cd558bbd5cf81365554a96afa512ffa1f1060d43c44c

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.367854Z digest=sha256:65e1f347be4daf6fb1abc8a9508118810640371ddec6baf171aaf3454abd5cb6

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.371800Z digest=sha256:1594202294d915001cbf2340a0b1601c7a88e2aa22236e611f88f7dc45572019

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.376494Z digest=sha256:87d07e9e1f68151b9bbb01d1d54fbe141febd825bd16f792ca49a5624d439e61

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.381212Z digest=sha256:e850aaaf430167b03ce2cd837f34c0c89287323df9e065971631984b802085a6

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.385273Z digest=sha256:4471c27959432f9b97f6504d941ade8d01c46bafd9cbd608299682745d04e54d

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.389683Z digest=sha256:1c342e3ed75cb9d9b2b11cb8c10bf0f87f8fd815062b0b6908f9d1283af8125c

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.393854Z digest=sha256:64198693a0c8bad95b4d70465aa91214957666296984e09b6a3eac59036c46c1

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.398051Z digest=sha256:21dc8a30911d4b3575a68f9411ca723c2817795c99bbb05604f6581d44eaaaf1

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.402093Z digest=sha256:52d806c9484e64bc76ae7fff32f76e45e556b977dc9f9e1637f494f2cfcf885b

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.406827Z digest=sha256:a5560423f4a27cb974753aa07dbd5cba093fb54c7c4894a232a59bb550783d4e

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.

source=pdf_text observed=2026-08-06T13:12:06.410938Z digest=sha256:9f87bd1af945f8cc8d019425726d11dc54f519c1b4778cc898f19bdb29200c7b

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.415351Z digest=sha256:02942529c6e8e90f074a264cd0f065fda306e62ae72265c23b9cde8da37e7e77

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-06T13:12:06.419450Z digest=sha256:31c693aab874d665755d07b7ea67295ad6314d0e35e16fe2022ee514d0046601

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:12:06.423775Z digest=sha256:6cb3c8fd394b9a99f1da438ddc2492c8ae649941bce38551262c2df12b4965db

Pith citing papers

No inbound Pith citation observations are available.