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Paper Citation Record · LEDGER

Feature-Enhanced TResNet for Fine-Grained Food Image Classification

As of 18 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2507.12828.

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

pith.paper-citation-record.v1
2507.12828 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

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measured 61 of 61 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

61 of 61 outbound references displayed

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External citation measurements

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Outbound references

Observation 4c8d4dc3-ee30-43cd-88f9-6f1a8fd39f1a · outbound

This paper cites Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning

Reference 1

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Observation 734fba19-b197-4b54-95b9-91afd53632d2 · outbound

This paper cites Deep learning for fine-grained classification of jujube fruit in the natural environment.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Deep learning for fine-grained classification of jujube fruit in the natural environment

Reference 2

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Observation 4f4b1c2d-d30f-4911-8118-da8d8baf1cf4 · outbound

This paper cites Fine-grained food classification methods on the uec food-100 database.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Fine-grained food classification methods on the uec food-100 database

Reference 3

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Observation ad24830c-f1af-40dc-a6ec-837b87b96498 · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Foodcswin: A high-accuracy food image recognition model for dietary assessment

Reference 4

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Observation 43aee6af-5c27-4d30-90b8-3cab8d1e585c · outbound

This paper cites Textural features for image classification.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Textural features for image classification

Reference 5

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Observation 8143eea2-c86f-450a-a503-75036bcc86cc · outbound

This paper cites On image classification: City images vs.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification On image classification: City images vs

Reference 6

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Observation b045b712-3743-480f-82fe-f6c3e4065890 · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Constrained nonnegative matrix factorization and hyperspectral image dimensionality reduction

Reference 7

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Observation 38a86444-d160-45b7-b2ed-73c013c61da4 · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification A spectral–spatial similarity-based method and its application to hyperspectral image classification

Reference 8

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Observation 7d785582-5a41-40e8-b6aa-75f28bcb73ab · outbound

This paper cites Deep convolutional neural networks for image classification: A comprehensive review.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Deep convolutional neural networks for image classification: A comprehensive review

Reference 9

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Observation f64d058f-ce3f-4648-bb7d-066b98e2f800 · outbound

This paper cites Deep learning for hyperspectral image classification: An overview.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Deep learning for hyperspectral image classification: An overview

Reference 10

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Observation ebc1fdc5-2807-458b-ad94-22ba2e854047 · outbound

This paper cites Survey on svm and their application in image classification.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Survey on svm and their application in image classification

Reference 11

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Observation 3668f18b-36e3-4636-b351-fb10f5d1cc54 · outbound

This paper cites Resnet in Resnet: Generalizing Residual Architectures.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Resnet in Resnet: Generalizing Residual Architectures

Reference 12

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Observation 330a8495-44c3-46a7-b6ee-ddb66aa96d10 · outbound

This paper cites Wider or deeper: Revisiting the resnet model for visual recognition.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Wider or deeper: Revisiting the resnet model for visual recognition

Reference 13

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Observation fd90c527-0ca2-4c51-98ff-e4944b18f061 · outbound

This paper cites Resnet 50.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Resnet 50

Reference 14

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Observation 17d36390-048b-4d66-96fc-6c35125bd5ac · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

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Observation 84c0bba8-538e-49eb-a63a-59d42220e9b5 · outbound

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Feature-Enhanced TResNet for Fine-Grained Food Image Classification Rethinking the inception architecture for computer vision

Reference 16

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Observation 53de949c-4f73-4935-a784-9054e0a088dc · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 17

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Feature-Enhanced TResNet for Fine-Grained Food Image Classification Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 18

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Observation 6fc71a49-0f39-41e4-8d95-d14693763fd4 · outbound

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Feature-Enhanced TResNet for Fine-Grained Food Image Classification Searching for mobilenetv3

Reference 19

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Application of improved convolutional neural network in medical image segmentation

Reference 20

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Application of improved convolutional neural network in lung image segmentation

Reference 21

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Observation 2169110e-0f86-49a1-81f6-854f4e956f46 · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Multi-view hierarchical split network for brain tumor segmentation

Reference 22

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Observation 28fc010a-dc45-4423-be6d-90b058349db6 · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Sr-net: A sequence offset fusion net and refine net for undersampled multislice mr image reconstruction

Reference 23

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Observation 776a7c6b-e4db-453f-af6a-d2318d1b46d8 · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Food image segmentation based on deep and shallow dual-branch network

Reference 24

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Observation 0bdefad0-8dc9-4798-812c-380e57f3e09f · outbound

This paper cites 3d u-net applied to simple attention module for head and neck tumor segmentation in pet and ct images.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification 3d u-net applied to simple attention module for head and neck tumor segmentation in pet and ct images

Reference 25

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms

Reference 26

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Feature-Enhanced TResNet for Fine-Grained Food Image Classification Swin attention augmented residual network: a fine-grained pest image recognition method

Reference 27

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Feature-Enhanced TResNet for Fine-Grained Food Image Classification Efficient combination of cnn and transformer for dual-teacher uncertainty-guided semi-supervised medical image segmentation

Reference 28

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Feature-Enhanced TResNet for Fine-Grained Food Image Classification Rmmlp:rolling mlp and matrix decomposition for skin lesion segmentation

Reference 29

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Feature-Enhanced TResNet for Fine-Grained Food Image Classification Light3dhs: A lightweight 3d hippocampus segmentation method using multiscale convolution attention and vision transformer

Reference 30

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Observation df5977ba-3417-4012-ad3b-f13680fbb24f · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification High accuracy food image classification via vision transformer with data augmentation and feature augmentation

Reference 31

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This paper cites Fine grained food image recognition based on swin transformer.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Fine grained food image recognition based on swin transformer

Reference 32

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Observation 3a7f08c0-494a-4fc6-b6f9-702045160e95 · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Highly scalable parallel genetic algorithm on sunway many-core processors

Reference 33

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Observation e6156dbb-e81f-4a82-a5e6-a899a72a7623 · outbound

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

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Fgfoodnet: Ingredient-perceived fine-grained food recognition for dietary monitoring

Reference 34

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Observation a9193d86-e1f6-4ec4-9f49-14fb7fcd6b5e · outbound

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Feature-Enhanced TResNet for Fine-Grained Food Image Classification Squeeze-and-excitation networks

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:48.857588Z digest=sha256:4733a14b68be45a333d9f7cbe20ad86ea2b3f5a4a74c858dd639516eb839f140

Observation 6b5d88aa-fab7-4c11-8ff3-e09117e143d9 · outbound

This paper cites Enhance via decoupling: Improving multi-label classifiers with variational feature augmentation.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Enhance via decoupling: Improving multi-label classifiers with variational feature augmentation

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-06T16:43:56.095178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:49.001156Z digest=sha256:1cd4833d0c47f637271f3b2fc33915675dd2a2e9e6c345ee7898123d8e300489

Observation 62515124-b2de-4953-b516-525163896976 · outbound

This paper cites Iml-gcn: Improved multi-label graph convolutional network for efficient yet precise image classification.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Iml-gcn: Improved multi-label graph convolutional network for efficient yet precise image classification

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-06T16:43:55.888595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:49.144548Z digest=sha256:ebb1724eea9bb14fb12166c565fb72a4c008e9ec05ad0544ee92e9c031581639

Observation 1cef3d07-c9dc-419c-a206-dbf7c3b623ef · outbound

This paper cites Diagnosis of alzheimer’s disease based on the modified tresnet.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Diagnosis of alzheimer’s disease based on the modified tresnet

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:55.513289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:49.515885Z digest=sha256:7c7762f3fecc27d48eb482f8719db843594a56de6eec742bd044831647ac1db8

Observation d9caece5-4b16-4a37-8f88-799fc5ae702d · outbound

This paper cites Research on x-ray image classification algorithm of covid-19 based on fs-tresn et model.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Research on x-ray image classification algorithm of covid-19 based on fs-tresn et model

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:55.303860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:49.640181Z digest=sha256:d043058c66fd2068b14a87f658b8356ee0ee06166c92c299903d40b40c6ec757

Observation ea879f63-c634-4e65-9e71-31b3b8cb9b7a · outbound

This paper cites Learn from each other to classify better: Cross-layer mutual attention learning for fine-grained visual classification.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Learn from each other to classify better: Cross-layer mutual attention learning for fine-grained visual classification

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:55.640760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:49.791815Z digest=sha256:06eee7802cfabff39daf66681adacf777c4ef6cb1e7e3c5ec6da0d673318f98c

Observation 1625f379-71f5-4ddb-b8af-4b398fd76c91 · outbound

This paper cites Transfg: A transformer architecture for fine-grained recognition.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Transfg: A transformer architecture for fine-grained recognition

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:55.139584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:49.932326Z digest=sha256:8c6cea6c5f0155e4ea5d670e33b347eac8a58e05aa00903d622d011635018ccd

Observation 7d7e4eca-6b7b-418a-b4b4-ee948fbd767a · outbound

This paper cites Tresnet: High performance gpu-dedicated architecture.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Tresnet: High performance gpu-dedicated architecture

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:54.955687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:50.095481Z digest=sha256:052e841796373e9d5570520ede70ba13059d0a4bbcf4d8515491042941e2f0c0

Observation 8efbfee0-f27b-4796-90e8-b65919ef301c · outbound

This paper cites Texture synthesis using convolutional neural networks.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Texture synthesis using convolutional neural networks

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:54.810780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:50.207838Z digest=sha256:d9407afc218e70b381708bcc80c76d46255ca7a0e213d5fdb53b2a454b1a4f26

Observation 675c022a-efb3-4901-aac1-bda7d8d811d0 · outbound

This paper cites Arbitrary style transfer in real-time with adaptive instance normalization.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Arbitrary style transfer in real-time with adaptive instance normalization

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:54.685113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:50.337943Z digest=sha256:f41971c6c83f5878eb94fe725919c8eff00d74819b7f5db6ad9f3cde930086c3

Observation c8cf7cbb-2169-462d-9607-b88a405c3ae1 · outbound

This paper cites Image style transfer using convolutional neural networks.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Image style transfer using convolutional neural networks

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:54.484400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:50.483737Z digest=sha256:91e6e35f08b2ab78685a5ea380d3b902017efbd7218043e504b52c4f03dbaf4e

Observation ef410a09-48a7-4373-87b3-f8749d1a6454 · outbound

This paper cites Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T16:43:50.581566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:50.581566Z digest=sha256:9de80e6f80fe9148a584901b97d362b3689f8f7ebeebaf711fea27b28725915f

Observation 1c0251be-0892-473a-84c4-f28f778cdcd6 · outbound

This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T16:43:50.761189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:50.761189Z digest=sha256:85ce120e4436f99888bfb98e7e207a1a983ba58990fe2c651e6969f02d8f3953

Observation 1746fc40-8af7-49db-a2f9-edf80ddb1f7f · outbound

This paper cites Srm: A style-based recalibration module for convolutional neural networks.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Srm: A style-based recalibration module for convolutional neural networks

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:54.268934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:50.911404Z digest=sha256:de70292b24a88c3ecb63d32767cc37321f0731ea6951c3aa7af14025b960dd1e

Observation 9fd1bf43-5aa6-4843-8910-4b7d192dd2fd · outbound

This paper cites Non-local neural networks.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Non-local neural networks

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T16:43:51.048012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:51.048012Z digest=sha256:e18dd21e55400217a30022f5b583f165f1939064dec88e06053cc6c552bdd4d7

Observation 52564cea-1ad7-4578-b5eb-f121bc02fa2d · outbound

This paper cites ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T16:43:51.263860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:51.263860Z digest=sha256:1fce31435709304ecbc29f8589e5fb314e7c07557019aa01040a366a5e7922b1

Observation a78bea4f-bf6e-4f12-8810-359687445468 · outbound

This paper cites Automatic chinese food recognition based on a stacking fusion model.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Automatic chinese food recognition based on a stacking fusion model

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:54.107531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:51.397220Z digest=sha256:81920d3e0f41737c632b562d2cfdba860ea31d37c5600ee7d564fe866cd8dca8

Observation a63b03b1-e517-4566-a1cc-6246cc1a0e37 · outbound

This paper cites Deep networks with stochastic depth.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Deep networks with stochastic depth

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T16:43:51.504981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:51.504981Z digest=sha256:3ab3b0d70982d6aadcc66ad7d7508c288bbf334ee83b028667beb2be6bed686b

Observation 8c1ebc84-518d-47a3-9cf9-e374d796f70e · outbound

This paper cites Improved adam optimizer for deep neural networks.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Improved adam optimizer for deep neural networks

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:53.944569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:51.642046Z digest=sha256:4221d73e8cd1dccabebb70747f429b6a9526b8cf178074d0bdd964b8e0216c5a

Observation 9e6a5159-a688-48c3-9649-7e32726407b1 · outbound

This paper cites On the Variance of the Adaptive Learning Rate and Beyond.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification On the Variance of the Adaptive Learning Rate and Beyond

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T16:43:51.780376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:51.780376Z digest=sha256:adf702b15eaefb0b603eb365a928447b8f1781038737375f3d76f12c3c2224cd

Observation f0902fc8-de7a-44b5-a830-d1adceef138f · outbound

This paper cites Deep classification with linearity-enhanced logits to softmax function.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Deep classification with linearity-enhanced logits to softmax function

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:53.708065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:51.940812Z digest=sha256:68f645da2127e4b4d563df771e7cc799b7b301b1235b18c6176644f0c5d42c7b

Observation db12dccc-eb57-4fac-be34-38f9eefb65e9 · outbound

This paper cites DenseNet: Implementing Efficient ConvNet Descriptor Pyramids.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T16:43:52.050809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:52.050809Z digest=sha256:89ec3652a5f65d387d30a4b6b5e4ed538757732aab0c84fe9f4b760a86c20b85

Observation 64ae88e3-da5b-4572-93e1-06c286f8986e · outbound

This paper cites Efficientnetv2: Smaller models and faster training.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Efficientnetv2: Smaller models and faster training

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:53.539069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:52.204537Z digest=sha256:56e9784e4103453dce0dda19500e4a3de7721e8ecb27fef8ef2f25e618dc663b

Observation 177ca168-7868-4a18-a697-f38564954072 · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:53.363349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:52.334508Z digest=sha256:c72dbb8d08d0ae1b37c5f8047b7e8931da6476e0970fae9c2cf1d1d30e27948c

Observation d2596f4b-8d09-4a65-8c83-41ac8310dd0c · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Xception: Deep learning with depthwise separable convolutions

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:53.245339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:52.507760Z digest=sha256:71473b6be05d4c5f56c9db823e998087bb2a4a3d02e02ca23e66b5a0a0beabd6

Observation 863b02a1-bc8b-4804-9742-606f74fc26a3 · outbound

This paper cites Xception: A technique for the experimental evaluation of dependability in modern computers.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Xception: A technique for the experimental evaluation of dependability in modern computers

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:53.027356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:52.589183Z digest=sha256:b2628c699dcc53bbab6c77dac160c08d21ffb0cc7e25217c43f7bbbcae124001

Observation f69a786e-cfa9-46e5-be86-3815f76ba31d · outbound

This paper cites Improved classification of different brain tumors in mri scans using patterned-gridmask.

Feature-Enhanced TResNet for Fine-Grained Food Image Classification Improved classification of different brain tumors in mri scans using patterned-gridmask

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:43:52.881784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T16:43:52.673820Z digest=sha256:1754c11f00529f408420763f22c1fcd97d91254d13b100a914c16bbe5bc48717

Pith citing papers

No inbound Pith citation observations are available.