Paper Citation Record · LEDGER

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2501.10080.

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

pith.paper-citation-record.v1
2501.10080 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:27:14.891510Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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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

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 7270c9d0-f398-43c6-ae30-29c1f9a6ca25 · outbound

This paper cites Slic superpix- 8 els compared to state-of-the-art superpixel methods.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Slic superpix- 8 els compared to state-of-the-art superpixel methods

Reference 1

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Observation 6a4c51ad-e15d-4ec4-8bac-1fa832442aea · outbound

This paper cites Deepcut: Unsupervised segmentation using graph neu- ral networks clustering.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Deepcut: Unsupervised segmentation using graph neu- ral networks clustering

Reference 2

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Observation 2373a789-fce2-4fbe-ba94-3db64c0e23dc · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Segnet: A deep convolutional encoder-decoder architecture for image segmentation

Reference 3

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Observation 5bea4f27-c056-4efd-be6a-2c57849efeea · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 4

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Observation 4d4920aa-3e0b-4afa-b236-fff85a734464 · outbound

This paper cites One- shot video object segmentation.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks One- shot video object segmentation

Reference 5

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Observation aac28672-4cf9-489c-bb36-87e143693afe · outbound

This paper cites Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges

Reference 6

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Observation c560984a-8602-4c01-aecb-61ff7fe20501 · outbound

This paper cites Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model

Reference 7

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Observation 41a5e0ee-1a6f-4516-8095-defdd490f85b · outbound

This paper cites Superpoint: Self-supervised interest point detection and description.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Superpoint: Self-supervised interest point detection and description

Reference 8

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Observation 821500bf-5b35-45df-a328-9428f2c7e0d7 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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Observation 5047b20d-ec89-4ee7-ac73-b7d28b9171b0 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Fast Graph Representation Learning with PyTorch Geometric

Reference 10

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Observation 25c0dc69-440c-4311-a820-b766bbd03a05 · outbound

This paper cites Inductive representation learning on large graphs.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Inductive representation learning on large graphs

Reference 11

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Observation ff827af1-51b9-4775-862f-49939a6b836d · outbound

This paper cites Mask r-cnn.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Mask r-cnn

Reference 12

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Observation 289c8efd-c527-4944-a505-4a8111362d50 · outbound

This paper cites A generative ap- pearance model for end-to-end video object segmentation.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks A generative ap- pearance model for end-to-end video object segmentation

Reference 13

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Observation 79ce1692-bb76-4da9-a4a1-cea01e2fa2a3 · outbound

This paper cites A review of graph neural networks: concepts, archi- tectures, techniques, challenges, datasets, applications, and future directions.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks A review of graph neural networks: concepts, archi- tectures, techniques, challenges, datasets, applications, and future directions

Reference 14

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Observation 6abc29aa-604d-43c9-b05b-bcd357ca2a06 · outbound

This paper cites Kingma and Jimmy Ba.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Kingma and Jimmy Ba

Reference 15

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Observation 6436bc68-bb7e-47d8-8864-d7649e66960f · outbound

This paper cites Kipf and Max Welling.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Kipf and Max Welling

Reference 16

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Observation ea43e6bb-df38-4aad-9504-a8942caeeaad · outbound

This paper cites Segment any- thing.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Segment any- thing

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:15.383078Z

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Observation 077a8ba2-7a42-4e8e-a4b5-a7c5519cf70e · outbound

This paper cites Microsoft coco: Common objects in context.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Microsoft coco: Common objects in context

Reference 18

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Observation 8d69b9cd-ec1c-4269-97d7-8e4ae91a5490 · outbound

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Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Isolation forest

Reference 19

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Observation 21ab6b12-f407-4177-bed9-550fcd824d49 · outbound

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

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 20

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Observation 98d4899f-e680-4c1f-82e6-732532d2c2ad · outbound

This paper cites Part-aware prototype network for few-shot semantic segmentation.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Part-aware prototype network for few-shot semantic segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:15.340489Z

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Observation 95dfed90-36d5-4780-9f2c-0548465f9b9c · outbound

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

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Swin transformer: Hierarchical vision transformer using shifted windows

Reference 22

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Observation c0957550-a6c8-4f26-8275-ed22780613d3 · outbound

This paper cites Image segmenta- tion using text and image prompts.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Image segmenta- tion using text and image prompts

Reference 23

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Observation c9475668-ebf7-4ba1-886f-fda3a685d0a3 · outbound

This paper cites Scaling open-vocabulary object detection.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Scaling open-vocabulary object detection

Reference 24

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Observation 0231e68f-dfcd-47bd-9c34-2d1d11b5e43c · outbound

This paper cites On First-Order Meta-Learning Algorithms.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks On First-Order Meta-Learning Algorithms

Reference 25

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Observation 194fe59b-3533-43bc-8184-48511c7c8117 · outbound

This paper cites Video object segmentation using space-time memory networks.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Video object segmentation using space-time memory networks

Reference 26

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Observation 619424d8-3cb0-4c93-b396-a810aae949d2 · outbound

This paper cites Pytorch: An im- perative style, high-performance deep learning library.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Pytorch: An im- perative style, high-performance deep learning library

Reference 27

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Observation eb75548e-74a3-4cc8-96cf-4bf109fb5c22 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks The 2017 DAVIS Challenge on Video Object Segmentation

Reference 28

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Observation 8924e860-c9a2-4250-8a28-465ec4fc1156 · outbound

This paper cites Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models

Reference 29

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Observation 6b7116f7-51c0-4801-8e0c-87c088556088 · outbound

This paper cites Segment Anything Meets Point Tracking.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Segment Anything Meets Point Tracking

Reference 30

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Observation c2015c42-e4b5-4f61-98f1-992a0cfdce0e · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 31

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

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks U- net: Convolutional networks for biomedical image segmen- tation

Reference 32

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Observation 6547c74b-15a1-4d82-bc13-b71363ec08ab · outbound

This paper cites SuperGlue: Learning feature matching with graph neural networks.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks SuperGlue: Learning feature matching with graph neural networks

Reference 33

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Observation 31f80b13-af82-4a47-871b-681c13156847 · outbound

This paper cites Metaseg: A survey of meta- learning for image segmentation.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Metaseg: A survey of meta- learning for image segmentation

Reference 34

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This paper cites Graph at- tention networks.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Graph at- tention networks

Reference 35

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Observation 96fd3608-9d04-4184-afca-9d863b050ab0 · outbound

This paper cites Ad- vances and challenges in meta-learning: A technical review.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Ad- vances and challenges in meta-learning: A technical review

Reference 36

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Observation 0f374d9f-6b33-4f39-8931-540fd383077c · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Images speak in images: A generalist painter for in-context visual learning

Reference 37

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Observation 9f958971-a25f-43f7-a4ff-9d2a6410b1b2 · outbound

This paper cites Seggpt: Towards seg- menting everything in context.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Seggpt: Towards seg- menting everything in context

Reference 38

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Observation bca96870-5b72-4840-9fd2-fd1e5a45be24 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 39

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Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Unresolved cited work

Reference 40

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Observation bc0b9616-db9f-473c-91e4-e195b5c9578c · outbound

This paper cites Efficient video object seg- mentation via network modulation.

Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks Efficient video object seg- mentation via network modulation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:15.117206Z

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Pith citing papers

No inbound Pith citation observations are available.