Feature-Enhanced TResNet for Fine-Grained Food Image Classification | Paper Citation Record

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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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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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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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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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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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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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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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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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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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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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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This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

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

Reference 18

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This paper cites Searching for mobilenetv3.

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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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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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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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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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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Observation c19f87e0-086f-4e68-bd91-39ffedff01ac · outbound

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

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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This paper cites Efficient combination of cnn and transformer for dual-teacher uncertainty-guided semi-supervised medical image segmentation.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:43:56.974748Z

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This paper cites Rmmlp:rolling mlp and matrix decomposition for skin lesion segmentation.

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

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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This paper cites Squeeze-and-excitation networks.

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

Reference 35

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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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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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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

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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

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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

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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

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