Paper Citation Record · LEDGER

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation

As of 14 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2507.17347.

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

pith.paper-citation-record.v1
2507.17347 v3

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:56:24.444106Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T01:26:06.001027Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

verified exact1
verified fuzzy39
unresolved13
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8089016e-2634-4b89-b3b5-20c19acbd96a · outbound

This paper cites Application of computer vision techniques to fermented foods: An overview.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Application of computer vision techniques to fermented foods: An overview

Reference 1

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Observation cff6d4ae-5cf8-49be-ab81-60e6baee450a · outbound

This paper cites Fgfoodnet: Ingredient-perceived fine-grained food recognition for dietary monitoring.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Fgfoodnet: Ingredient-perceived fine-grained food recognition for dietary monitoring

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.317090Z

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Observation 33512c1d-599b-4c16-823a-405a53e76a32 · outbound

This paper cites Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.301174Z

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Observation 096f7689-6ecd-4baa-b492-1482cacf9c94 · outbound

This paper cites Swin attention augmented residual network: a fine-grained pest image recognition method.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Swin attention augmented residual network: a fine-grained pest image recognition method

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.282808Z

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Observation f57b5f1e-ba44-46d5-a35a-c794aab2032f · outbound

This paper cites Highly scalable parallel genetic algorithm on sunway many-core processors.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Highly scalable parallel genetic algorithm on sunway many-core processors

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.267243Z

Source-reported events for the cited work

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Observation aa36ce29-fc87-482f-b5d2-f836e1ee25cf · outbound

This paper cites an unresolved cited work.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Unresolved cited work

Reference 6

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Observation e0a57a8c-f815-48d4-915a-0814a2859b68 · outbound

This paper cites Light3dhs: A lightweight 3d hippocampus segmen- tation method using multiscale convolution attention and vision transformer.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Light3dhs: A lightweight 3d hippocampus segmen- tation method using multiscale convolution attention and vision transformer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.235612Z

Source-reported events for the cited work

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Observation 0d55332a-3239-46f1-87b1-e2c7f9f21109 · outbound

This paper cites FoodSAM: Any Food Segmentation.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation FoodSAM: Any Food Segmentation

Reference 8

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Observation 7294e224-c78b-4436-9c85-0d52d8237180 · outbound

This paper cites Segment Anything.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Segment Anything

Reference 9

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Source-reported events for the cited work

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Observation abcd372a-e97a-485e-a2c5-1a247d6eacc6 · outbound

This paper cites P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks, 2022.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.219216Z

Source-reported events for the cited work

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Observation ded936ae-c49a-45ab-aca9-d31189451b44 · outbound

This paper cites Compacter: Efficient low-rank hypercomplex adapter layers, 2021.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Compacter: Efficient low-rank hypercomplex adapter layers, 2021

Reference 11

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Observation a451ce1c-8237-4dd6-b637-10650fce9663 · outbound

This paper cites Making pre-trained language models better few-shot learners.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Making pre-trained language models better few-shot learners

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.191847Z

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Observation 10890059-b49a-4fb3-80ca-425998524821 · outbound

This paper cites Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification, 2022.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification, 2022

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.176566Z

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Observation b05487f0-17a8-4665-ab54-980bec6d2fc5 · outbound

This paper cites Msp: Multi-stage prompting for making pre-trained language models better translators, 2022.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Msp: Multi-stage prompting for making pre-trained language models better translators, 2022

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.160893Z

Source-reported events for the cited work

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Observation 33ce62d1-b612-49f5-a0a0-1731e17630ba · outbound

This paper cites Mopeft: A mixture-of-pefts for the segment anything model, 2024.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Mopeft: A mixture-of-pefts for the segment anything model, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.144884Z

Source-reported events for the cited work

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Observation 9a2ff586-cfd4-4e49-85c8-7e80cb36187e · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows, 2021.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Swin transformer: Hierarchical vision transformer using shifted windows, 2021

Reference 16

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Source-reported events for the cited work

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Observation aface970-389b-44f2-ad0d-df0cb53b54c9 · outbound

This paper cites A large-scale benchmark for food image segmentation.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation A large-scale benchmark for food image segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.116041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eee3b443-1609-4acf-aec5-2e6d9e36fc9f · outbound

This paper cites UEC-FoodPIX Complete: A large-scale food image segmentation dataset.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation UEC-FoodPIX Complete: A large-scale food image segmentation dataset

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.099657Z

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Observation f6746071-bdcd-4c45-8b42-0e72fb1ced2a · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 19

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Source-reported events for the cited work

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Observation d98213a4-967c-4895-aa65-0332d9534269 · outbound

This paper cites Application of improved convolutional neural network in medical image segmentation.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Application of improved convolutional neural network in medical image segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.072841Z

Source-reported events for the cited work

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Observation 696e3e8d-5988-4d4b-b524-4a44f9c52378 · outbound

This paper cites Application of improved convolutional neural network in lung image segmentation.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Application of improved convolutional neural network in lung image segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.058300Z

Source-reported events for the cited work

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Observation ea3d68a7-a0fa-43ac-a292-4ade632da1b8 · outbound

This paper cites Sr-net: A sequence offset fusion net and re- fine net for undersampled multislice mr image reconstruction.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Sr-net: A sequence offset fusion net and re- fine net for undersampled multislice mr image reconstruction

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.043817Z

Source-reported events for the cited work

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Observation f8fa2161-90cf-4c1a-8afb-45a055d1b8c5 · outbound

This paper cites Multi-view hierarchical split network for brain tumor segmentation.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Multi-view hierarchical split network for brain tumor segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:25.029311Z

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Observation 36b80677-b944-48a1-867a-0f829a0316bd · outbound

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Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Unresolved cited work

Reference 24

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Source-reported events for the cited work

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Observation 7983a69b-9c21-4258-a035-923f14ff0b7f · outbound

This paper cites Semi-supervised ct image segmentation via contrastive learning based on entropy constraints.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Semi-supervised ct image segmentation via contrastive learning based on entropy constraints

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.999369Z

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Observation e1be363a-b1e6-42de-be86-db36daaa4063 · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, 2021.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, 2021

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.984855Z

Source-reported events for the cited work

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Observation fcc852b5-b2f1-4bd0-97da-39dd299bbdef · outbound

This paper cites Constrained nonnegative matrix factorization and hyperspectral image dimensionality reduction.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Constrained nonnegative matrix factorization and hyperspectral image dimensionality reduction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.969078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9da9853f-b3b4-47c9-b350-05d3a44f6eb7 · outbound

This paper cites A spectral–spatial similarity-based method and its application to hyperspectral image classifica- tion.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation A spectral–spatial similarity-based method and its application to hyperspectral image classifica- tion

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.953542Z

Source-reported events for the cited work

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Observation d7379806-b9b8-4106-942c-479bad419d78 · outbound

This paper cites High accuracy food image classification via vision transformer with data augmentation and feature augmentation.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation High accuracy food image classification via vision transformer with data augmentation and feature augmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.937978Z

Source-reported events for the cited work

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Observation fad6beed-ec5f-4ed0-bbfd-4f4e43011f03 · outbound

This paper cites Foodcswin: A high-accuracy food image recognition model for dietary assessment.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Foodcswin: A high-accuracy food image recognition model for dietary assessment

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.921551Z

Source-reported events for the cited work

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Observation b89505cb-ae6f-4384-85c3-4927d502b1b4 · outbound

This paper cites Fine grained food image recognition based on swin trans- former.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Fine grained food image recognition based on swin trans- former

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.902719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 54f86b69-88c9-43da-9323-c5feb809e2bc · outbound

This paper cites Ovfoodseg: Elevating open-vocabulary food image segmentation via image-informed textual representation, 2024.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Ovfoodseg: Elevating open-vocabulary food image segmentation via image-informed textual representation, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.886711Z

Source-reported events for the cited work

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Observation f7e7e243-cbb0-45fb-99c9-4cc07e016b22 · outbound

This paper cites Canet: cross attention network for food image segmentation.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Canet: cross attention network for food image segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.870211Z

Source-reported events for the cited work

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Observation 8b6d9c85-cb2f-49c7-b0b8-74ded5c76a82 · outbound

This paper cites Food image segmentation based on deep and shallow dual-branch network.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Food image segmentation based on deep and shallow dual-branch network

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.851815Z

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Observation 800db7c9-e848-4834-a5ae-6edde83723e2 · outbound

This paper cites Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.834864Z

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Observation b98ebc6a-143b-4e9e-8cb2-f4037e686df6 · outbound

This paper cites Catastrophic interference in connectionist networks: The sequential learning problem.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Catastrophic interference in connectionist networks: The sequential learning problem

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:24.349805Z

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Observation fcf337de-986a-4d53-8b6f-2f59e1cc8bd0 · outbound

This paper cites Mitigating the alignment tax of rlhf, 2024.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Mitigating the alignment tax of rlhf, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.808353Z

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Observation da9efa1a-7f6b-4cf9-8335-7fba5048788e · outbound

This paper cites Tsaftaris, and Timothy Hospedales.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Tsaftaris, and Timothy Hospedales

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.791305Z

Source-reported events for the cited work

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Observation 72b5030a-2529-493e-9a9d-48b9b2113e79 · outbound

This paper cites Parameter-efficient transfer learning for nlp, 2019.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Parameter-efficient transfer learning for nlp, 2019

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.773464Z

Source-reported events for the cited work

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Observation 951c3ef7-4fda-45a1-beda-d75b624ae51b · outbound

This paper cites Adapterfusion: Non- destructive task composition for transfer learning, 2021.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Adapterfusion: Non- destructive task composition for transfer learning, 2021

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.754948Z

Source-reported events for the cited work

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Observation ba7d6b5f-14e7-433a-926e-e997a5f68372 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:24.374959Z

Source-reported events for the cited work

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Observation 7441e8c4-cf49-4076-94de-d16b653601e3 · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models, 2022.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models, 2022

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.724461Z

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Observation 4ab297f4-9072-4af4-8878-a765ef62b3cd · outbound

This paper cites Visual prompt tuning, 2022.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Visual prompt tuning, 2022

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:24.386546Z

Source-reported events for the cited work

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Observation 8c6f1ff8-6633-41b4-b4f4-3009bf26fdd3 · outbound

This paper cites Three things everyone should know about vision transformers, 2022.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Three things everyone should know about vision transformers, 2022

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.688670Z

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Observation 050daff2-8c99-4e9b-9d6e-2a2acfc273dd · outbound

This paper cites AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:24.396891Z

Source-reported events for the cited work

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Observation ebb129f6-d789-467f-8f43-e62b62dfd9bc · outbound

This paper cites 5%>100%: Breaking performance shackles of full fine-tuning on visual recognition tasks, 2024.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation 5%>100%: Breaking performance shackles of full fine-tuning on visual recognition tasks, 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.670995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 23634fea-a664-4ad6-9fe4-08b43f9a641a · outbound

This paper cites Very deep convolutional networks for large-scale image recognition, 2015.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Very deep convolutional networks for large-scale image recognition, 2015

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:24.408891Z

Source-reported events for the cited work

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Observation 35a20d2a-0614-487d-a687-0d0f9441e0b4 · outbound

This paper cites Visualizing and understanding convolutional networks, 2013.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Visualizing and understanding convolutional networks, 2013

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:24.415854Z

Source-reported events for the cited work

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Observation fd257ce1-aef4-45cd-b713-464bcc38e261 · outbound

This paper cites Deep residual learning for image recognition, 2015.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Deep residual learning for image recognition, 2015

Reference 49

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Observation 4483c1d9-aae4-47a1-a10c-5414b10dad4a · outbound

This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark, 2020.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark, 2020

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.617337Z

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Observation 1adb2a40-5c00-4871-88f3-73c95e30d4fb · outbound

This paper cites Gourmetnet: Food segmentation using multi-scale waterfall features with spatial and channel attention.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Gourmetnet: Food segmentation using multi-scale waterfall features with spatial and channel attention

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:56:24.598967Z

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Observation 62496e3b-4925-4f3f-bd83-1bb8356d4265 · outbound

This paper cites Bayesian deep learning for semantic segmentation of food images.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Bayesian deep learning for semantic segmentation of food images

Reference 52

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Observation 42b8782d-5365-4769-93fe-8cb828291097 · outbound

This paper cites Large scale visual food recognition, 2023.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation Large scale visual food recognition, 2023

Reference 53

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Pith citing papers

Observation 3127a464-8430-4580-b8d7-3c34ee9500c9 · inbound

Food Image Segmentation with LLM-Derived Ingredient Labels and Multimodal Fusion cites this paper.

Food Image Segmentation with LLM-Derived Ingredient Labels and Multimodal Fusion Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation

Reference 1

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