Paper Citation Record · LEDGER

Class-specific diffusion models improve military object detection in a low-data domain

As of 4 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2604.18076.

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

pith.paper-citation-record.v1
2604.18076 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T04:34:29.790101Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

33 of 33 outbound references displayed

verified exact3
verified fuzzy29
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 229b995c-f45c-458b-aa98-28b4e7442395 · outbound

This paper cites On the use of simulated data for target recognition and mission planning.

Class-specific diffusion models improve military object detection in a low-data domain On the use of simulated data for target recognition and mission planning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.536049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a3a856d6-5064-4328-9ee0-9ab05c218afb · outbound

This paper cites Generative AI methods for synthesis of image data to train AI for automated scene understanding in a military context: a review of opportunities.

Class-specific diffusion models improve military object detection in a low-data domain Generative AI methods for synthesis of image data to train AI for automated scene understanding in a military context: a review of opportunities

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.571682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0fc807ba-ac91-4351-bf07-22d5a58439b4 · outbound

This paper cites Synthetic Data from Diffusion Models Improves ImageNet Classification.

Class-specific diffusion models improve military object detection in a low-data domain Synthetic Data from Diffusion Models Improves ImageNet Classification

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:15:22.992027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ece8cb82-c84d-4243-9823-35a61748d153 · outbound

This paper cites DiffusionDet: Diffusion model for object detection.

Class-specific diffusion models improve military object detection in a low-data domain DiffusionDet: Diffusion model for object detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.522130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ade9997a-222e-40fa-9b9a-70c9ac70bdf3 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Class-specific diffusion models improve military object detection in a low-data domain Lora: Low-rank adaptation of large language models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.528775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 42869aac-50ab-4d54-8261-e4082f971c16 · outbound

This paper cites T-LoRA: Single image diffusion model cus- tomization without overfitting.

Class-specific diffusion models improve military object detection in a low-data domain T-LoRA: Single image diffusion model cus- tomization without overfitting

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.537555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 81ccb105-0670-4add-a7a3-7c23891bc14c · outbound

This paper cites The effect of simulation variety on a deep learning-based military vehicle detector.

Class-specific diffusion models improve military object detection in a low-data domain The effect of simulation variety on a deep learning-based military vehicle detector

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.555499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 372160ce-4dd9-4960-b675-b1d2638deb93 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Class-specific diffusion models improve military object detection in a low-data domain Adding conditional control to text-to-image diffusion models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.558790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9d3d687a-83b7-4d73-ad6f-c8c6d71b63d9 · outbound

This paper cites Combining simulated data, foundation models, and few real samples for training object detectors.

Class-specific diffusion models improve military object detection in a low-data domain Combining simulated data, foundation models, and few real samples for training object detectors

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.542982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ae316baf-9c4e-4c0d-86ee-71c543eaff18 · outbound

This paper cites Unlocking thermal aerial imaging: Synthetic enhancement of UAV datasets.

Class-specific diffusion models improve military object detection in a low-data domain Unlocking thermal aerial imaging: Synthetic enhancement of UAV datasets

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.546481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7b68ca79-5a4c-4b5d-b030-fb3d605ef81e · outbound

This paper cites 3DSM- COS: A 3D model-based synthetic data pipeline for military camouflaged object segmentation with distractor-augmented realism.

Class-specific diffusion models improve military object detection in a low-data domain 3DSM- COS: A 3D model-based synthetic data pipeline for military camouflaged object segmentation with distractor-augmented realism

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.578231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b3e42159-0195-4ead-90cb-edae106b9973 · outbound

This paper cites Balancing 3D-model fidelity for training a vehicle detector on simulated data.

Class-specific diffusion models improve military object detection in a low-data domain Balancing 3D-model fidelity for training a vehicle detector on simulated data

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.500496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 28146605-edde-44e1-8bb7-a6a618300b6a · outbound

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

Class-specific diffusion models improve military object detection in a low-data domain AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:15:22.996101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3e87e6b6-6404-4b62-9dc0-dbc425868302 · outbound

This paper cites Improving object detector training on synthetic data by starting with a strong baseline methodology.

Class-specific diffusion models improve military object detection in a low-data domain Improving object detector training on synthetic data by starting with a strong baseline methodology

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.565335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 08ebbceb-98b1-4614-b42c-3522a7c665f2 · outbound

This paper cites Impact of style transfer approaches on synthetic data for military camouflaged object detection.

Class-specific diffusion models improve military object detection in a low-data domain Impact of style transfer approaches on synthetic data for military camouflaged object detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.520531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2ad53488-ed2c-43f4-b93d-2d4a4921c452 · outbound

This paper cites Generative adversarial networks.

Class-specific diffusion models improve military object detection in a low-data domain Generative adversarial networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.546152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f658bfa3-858d-4fe9-a32a-baba0987ad60 · outbound

This paper cites Implicit multi-spectral transformer: An lightweight and effective visible to infrared image translation model.

Class-specific diffusion models improve military object detection in a low-data domain Implicit multi-spectral transformer: An lightweight and effective visible to infrared image translation model

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.493377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 22cf543c-bb93-4bf5-a700-01ecbeae0eb0 · outbound

This paper cites InfraGAN: A GAN architecture to transfer visible images to infrared domain.

Class-specific diffusion models improve military object detection in a low-data domain InfraGAN: A GAN architecture to transfer visible images to infrared domain

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.519102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 75e07075-74f6-49ce-9599-c467f26eceb8 · outbound

This paper cites CycleGAN-based realistic image dataset generation for forward-looking sonar.

Class-specific diffusion models improve military object detection in a low-data domain CycleGAN-based realistic image dataset generation for forward-looking sonar

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.554753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 85d83dc3-9b73-4a0e-bf99-a9fa9108b4f5 · outbound

This paper cites F., “FLUX.”https://github.com/black-forest-labs/flux(2024).

Class-specific diffusion models improve military object detection in a low-data domain F., “FLUX.”https://github.com/black-forest-labs/flux(2024)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.548938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5d727d04-4936-4bb9-bcb3-7962dd695cb8 · outbound

This paper cites Data augmentation for vehicle detection with diffusion-based object inpainting.

Class-specific diffusion models improve military object detection in a low-data domain Data augmentation for vehicle detection with diffusion-based object inpainting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.561993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e20c7f5c-ca36-4a5a-9b29-196b4022684f · outbound

This paper cites Kimi-VL Technical Report.

Class-specific diffusion models improve military object detection in a low-data domain Kimi-VL Technical Report

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:06:31.748869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b51f724b-d881-45f0-ad20-e3b71a8bcab5 · outbound

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

Class-specific diffusion models improve military object detection in a low-data domain Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.574900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8562db56-f9ef-4dac-8fe9-f567a9be447a · outbound

This paper cites O.,Blender - a 3D modelling and rendering package.

Class-specific diffusion models improve military object detection in a low-data domain O.,Blender - a 3D modelling and rendering package

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.551672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b018d89-3507-4824-b06e-903e7ec357b9 · outbound

This paper cites com/en/products/pd/equipment/scanners/ fi-8950-production-scanner.

Class-specific diffusion models improve military object detection in a low-data domain com/en/products/pd/equipment/scanners/ fi-8950-production-scanner

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T12:15:22.984081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 22c39c31-edb3-48c0-bde9-9842e23701b2 · outbound

This paper cites DINOv2: Learning robust visual features without supervision.

Class-specific diffusion models improve military object detection in a low-data domain DINOv2: Learning robust visual features without supervision

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.549503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3e14ebb6-a4d1-4d32-ab6f-f16e4863ac27 · outbound

This paper cites Ultralytics yolo.

Class-specific diffusion models improve military object detection in a low-data domain Ultralytics yolo

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.517626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 50571de8-36e2-4e56-8513-00127bbe08d3 · outbound

This paper cites A survey on performance metrics for object-detection algorithms.

Class-specific diffusion models improve military object detection in a low-data domain A survey on performance metrics for object-detection algorithms

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.557583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 03a0e8cc-f4d0-429a-ad2e-cd8ba7403b18 · outbound

This paper cites Conditioning diffusion models via attributes and semantic masks for face generation.

Class-specific diffusion models improve military object detection in a low-data domain Conditioning diffusion models via attributes and semantic masks for face generation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.552426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0dee302b-fa65-4cf8-acd9-95c915db9615 · outbound

This paper cites Loosecontrol: Lifting controlnet for generalized depth conditioning.

Class-specific diffusion models improve military object detection in a low-data domain Loosecontrol: Lifting controlnet for generalized depth conditioning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.543232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 75e776d4-ea8a-4c19-9a3a-68c0827e217f · outbound

This paper cites Advancing state of the art object detection (again) with RF-DETR.

Class-specific diffusion models improve military object detection in a low-data domain Advancing state of the art object detection (again) with RF-DETR

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.525518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f43e20c1-b60e-4387-b267-4211aebaeff7 · outbound

This paper cites Tide: A general toolbox for identifying object detection errors.

Class-specific diffusion models improve military object detection in a low-data domain Tide: A general toolbox for identifying object detection errors

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.568472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5d5e0138-5500-4232-8130-6fda4bcc25a4 · outbound

This paper cites The corresponding user prompt is reported in Listing 2.

Class-specific diffusion models improve military object detection in a low-data domain The corresponding user prompt is reported in Listing 2

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T04:34:36.533085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

No inbound Pith citation observations are available.