Paper Citation Record · LEDGER

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity

As of 17 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.03754.

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

pith.paper-citation-record.v1
2607.03754 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T00:11:33.168015Z

measured 32 of 32 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

32 of 32 outbound references displayed

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unresolved32
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Outbound references

Observation 41ce54c9-ef9e-4d2d-b7e4-a3781c996e6c · outbound

This paper cites A semantic segmentation dataset and real-time localization model for anti-uav applications.Applied Sciences, 15(13):7183, 2025.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity A semantic segmentation dataset and real-time localization model for anti-uav applications.Applied Sciences, 15(13):7183, 2025

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation f2268c80-dd19-41fe-9e5f-1cc8cdfbb0a1 · outbound

This paper cites Improved u-net with identity transformer encoder for efficient uav semantic segmentation.IEEE Access, 13:208962–208972, 2025.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Improved u-net with identity transformer encoder for efficient uav semantic segmentation.IEEE Access, 13:208962–208972, 2025

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation d5f77758-ba9c-46f6-a527-995bf80e4999 · outbound

This paper cites Segment anything.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Segment anything

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 5f1c1f5d-05d5-4b9e-a251-3d2662d3a371 · outbound

This paper cites Sam 2: Segment anything in images and videos.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Sam 2: Segment anything in images and videos

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 91227fe7-88cc-40d8-85c4-0554f786821e · outbound

This paper cites SAM 3: Segment Anything with Concepts.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity SAM 3: Segment Anything with Concepts

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation d40c7c9f-df72-4b9e-bc9c-c2f9c1286fa7 · outbound

This paper cites Metaformer baselines for vision.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(2):896–912, 2023.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Metaformer baselines for vision.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(2):896–912, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 84b7844d-46d8-4158-8b98-4db4c5b8b5c4 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 822e9123-0cb1-4e8d-9e3a-fa7f2cda5665 · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 0980f256-4007-4817-8730-78fecbb19f46 · outbound

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

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity U-net: Convolutional networks for biomedical image segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 4221e963-b3f3-4c33-a06b-85933dd1ebed · outbound

This paper cites Robust u-net-based road lane markings detection for autonomous driving.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Robust u-net-based road lane markings detection for autonomous driving

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 8b4fea43-fafc-4342-9f40-6495387fe1c5 · outbound

This paper cites A novel encoder-decoder network with guided transmission map for single image dehazing.Procedia Computer Science, 204:682–689, 2022.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity A novel encoder-decoder network with guided transmission map for single image dehazing.Procedia Computer Science, 204:682–689, 2022

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 498e0b3f-6093-4947-a2a1-04eaa2498277 · outbound

This paper cites Encoder-decoder networks with guided transmission map for effective image dehazing.The Visual Computer, 41:359–382, 2025.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Encoder-decoder networks with guided transmission map for effective image dehazing.The Visual Computer, 41:359–382, 2025

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 2cc234bc-b1da-4b99-a50f-87427c83333d · outbound

This paper cites Spatial pyramid pooling in deep convolutional networks for visual recognition.IEEE transactions on pattern analysis and machine intelligence, 37(9): 1904–1916, 2015.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Spatial pyramid pooling in deep convolutional networks for visual recognition.IEEE transactions on pattern analysis and machine intelligence, 37(9): 1904–1916, 2015

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 1052aada-8967-4ad8-9338-66092468108a · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation c81970a3-9ba3-4448-aac8-1cbe1b9e52ac · outbound

This paper cites Semi-supervised semantic segmenta- tion needs strong, varied perturbations.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Semi-supervised semantic segmenta- tion needs strong, varied perturbations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation b89f8436-2865-414e-8480-f4866a958de3 · outbound

This paper cites Semi-supervised semantic segmentation with cross- consistency training.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Semi-supervised semantic segmentation with cross- consistency training

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 488df3a3-2b7b-4f83-9d20-93a26df9ff90 · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 5c80101a-3957-4413-a4e0-5f3c1f4bb5b9 · outbound

This paper cites Self-supervised augmentation consistency for adapting semantic segmentation.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Self-supervised augmentation consistency for adapting semantic segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation ad7d0eff-caac-4361-a7b9-27e3d21a0a8c · outbound

This paper cites Self-supervised learning of object parts for semantic segmentation.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Self-supervised learning of object parts for semantic segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 740b24f3-41bb-4187-9e90-5b27ce7870b9 · outbound

This paper cites $\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity $\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation a12417f5-afd3-4e2b-bb9e-80b3363d299e · outbound

This paper cites Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 5c600864-aaf2-4bfc-8f0f-470358b4a5d2 · outbound

This paper cites Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation ecd072d5-f6f6-46b1-a110-61febe658a83 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 57097e98-8016-46aa-add1-19504663ba30 · outbound

This paper cites Pyramid scene parsing network.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Pyramid scene parsing network

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation c17dba27-117d-419e-b893-a63437d98e12 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Unet++: A nested u-net architecture for medical image segmentation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 9591db03-1f4e-4f1b-837b-e6d0838e2b74 · outbound

This paper cites Road extraction by deep residual u-net.IEEE Geoscience and Remote Sensing Letters, 15(5):749–753, 2018.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Road extraction by deep residual u-net.IEEE Geoscience and Remote Sensing Letters, 15(5):749–753, 2018

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 94829836-5e6f-4f80-a84a-281ee9f538c5 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 60c1f595-47cb-4f2d-a754-d7f2820d26a4 · outbound

This paper cites Resunet++: An advanced architecture for medical image segmentation.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Resunet++: An advanced architecture for medical image segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 0ac95261-6753-4ad6-ace9-6744152f2f23 · outbound

This paper cites Distilled pooling transformer encoder for efficient realistic image dehazing.Neural Computing and Applications, 37(6):5203–5221, 2025.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Distilled pooling transformer encoder for efficient realistic image dehazing.Neural Computing and Applications, 37(6):5203–5221, 2025

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation f9322f54-6781-404a-a896-ee2e23407b1d · outbound

This paper cites Fill-unet: extended composite semantic segmentation.Applied Soft Computing, 172:112891, 2025.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Fill-unet: extended composite semantic segmentation.Applied Soft Computing, 172:112891, 2025

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation be5269ad-24d2-460b-b243-f90cc63b088a · outbound

This paper cites Unpaired image dehazing via kolmogorov-arnold transformation of latent features.Pattern Recognition, page 113304, 2026.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Unpaired image dehazing via kolmogorov-arnold transformation of latent features.Pattern Recognition, page 113304, 2026

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Observation 4a68b870-7fb3-4b80-bbc0-0277c30ea995 · outbound

This paper cites Improving convolutional networks with self-calibrated convolutions.

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity Improving convolutional networks with self-calibrated convolutions

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-12T00:11:33.168015Z

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Pith citing papers

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