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Paper Citation Record · LEDGER

Feature-Enhanced TResNet for Fine-Grained Food Image Classification

As of 8 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.

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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

This paper cites Rethinking the inception architecture for computer vision.

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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Observation aae543ed-8987-4c6b-9d12-db3ac2b5564e · outbound

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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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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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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no resolver link, observed 2026-08-06T16:43:48.857588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:43:48.857588Z digest=sha256:2c3f137ddd3135c9380313034afb15cb55476c43840bb2da0a4b195433b3c5e2

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

Resolution
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:49.001156Z digest=sha256:2b516ed5ef06e14f73e0ca3b3df523d80b73ff012f585ff0ca68fd8451cce919

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

Resolution
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:49.144548Z digest=sha256:40533f5a658e3a449ed8be64fd65dfd33e7c514aea88c8842a801bd3331b7cfe

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:49.515885Z digest=sha256:ce1b442ad420203e0236537a284d5280fbc49bc8a3b3db25f68c256f7c9cc7f4

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:49.640181Z digest=sha256:de4e849e7c0801d536c739674360fd83d85846f8d4b28b183ee31470606e42e0

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:49.791815Z digest=sha256:bd45eaf1199361613db9cf6e343735271561d0fdce5f1f5e6313acda2c805726

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:49.932326Z digest=sha256:6986c9d6b57ff0b1bfa3d399df8e5e4a09a5d340c8c65e7e4dc507cf27492a39

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:50.095481Z digest=sha256:b145f4a3e6ca40dd79bfa05f821f19b968d6b0b7f1f81be8b6a1b784213600c3

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:50.207838Z digest=sha256:4879d146d82e48e4cddaaa3cf452060d306d8711526e1d87aa2f6ef8096215b0

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:50.337943Z digest=sha256:4a32e0fc0bffe6ba75c9860c98d9b37be6448c6aa3ef1b442057614cdc2d6bac

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:50.483737Z digest=sha256:9b989356d2e07b9c778105163b1f4e6e2de30be7862bdcfe94354862d0186c94

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:fc75990b7df3ed4d1b44e70b8c0a1fff7d347afb60cfeed86868eea5e65eaf51

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=sha256:0940de4373ecd911bb1466e3ac3703391e0aacb5dc52ccd88a04fa2e3946db89

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:50.911404Z digest=sha256:dbd145d9288775dc6ecf0f45728ff72ea5f57632882cfce4e86e826f9e89a0a8

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:af10acebefce4f1fd2fa4d06ebb6acc4017cb86757d2a644b0d43f5b76348f6f

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:d4234900ed1407dbd5a9d896ad9aad77b14a29cf673553a24aa343c926815fa7

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:51.397220Z digest=sha256:fe5d8053f92928cbb8e62dc0d1121347b69dd4eae6d322c2822102f062beb6cb

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:dac7c9c5340df7d2d9c625af10c546f0756cf07eb7855413e5dc883495bd3bcb

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:51.642046Z digest=sha256:027854ae04b82a56ad4029b12064022d31138b4d461aef670b4c17888703778c

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=sha256:74c49ee870b3d3105bcafea03db6129f9e2e56b3a71fe38431464f539e9f57f7

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:51.940812Z digest=sha256:fb86607bba0634f08bbaf98d8e39f6cd4ee7a2398e573c4f548419f94d287362

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=sha256:1aa222b2749cc56a87777937a04a8350d7117bc541bcdd4a051906493d2866d0

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:52.204537Z digest=sha256:9b556d59dd3f6bdc485c933fd5fafcf6af8bac485969b8d37cef93fa18b98b2a

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:52.334508Z digest=sha256:949df071a1266052221a30e7317ab6bf9f5b270b9745afdb0f2a082ac5d5992b

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:52.507760Z digest=sha256:28d647ade5bef43eef7642233bfdfdca151aa98cd9d9bc15b9aee4ac0f938fef

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:52.589183Z digest=sha256:75a0151112c8f18e18b9047c7113c034c8f23a45804791f2bd0607fc4b7c7ef4

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:43:52.673820Z digest=sha256:cbf5510b26d7c47008e51b8fc76778bb55c8595e750b8c96a84ff87c001f9f46

Pith citing papers

No inbound Pith citation observations are available.