Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders | Paper Citation Record

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This paper cites write newline.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders write newline

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This paper cites Data Driven Approach to Leaf Recognition : Logistic Regression for Smart Agriculture.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Data Driven Approach to Leaf Recognition : Logistic Regression for Smart Agriculture

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This paper cites Grape bunch and vine trunk dataset for Deep Learning object detection., July 2021.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Grape bunch and vine trunk dataset for Deep Learning object detection., July 2021

Reference 3

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This paper cites Al-khazraji, Mohammed Abdallazez Mohammed, Dhafar Hamed Abd, Wasiq Khan, Bilal Khan, and Abir Jaafar Hussain.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Al-khazraji, Mohammed Abdallazez Mohammed, Dhafar Hamed Abd, Wasiq Khan, Bilal Khan, and Abir Jaafar Hussain

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders ESCA -dataset

Reference 5

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This paper cites The Hidden Uniform Cluster Prior in Self-Supervised Learning.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders The Hidden Uniform Cluster Prior in Self-Supervised Learning

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders A Cookbook of Self-Supervised Learning

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This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

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This paper cites VINEyard Piacenza Image Collections - VINEPICs , April 2023.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders VINEyard Piacenza Image Collections - VINEPICs , April 2023

Reference 9

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This paper cites EVALUATING DATA AUGMENTATION FOR GRAPEVINE VARIETIES IDENTIFICATION.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders EVALUATING DATA AUGMENTATION FOR GRAPEVINE VARIETIES IDENTIFICATION

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Carneiro, Ana Texeira, Raul Morais, Joaquim J

Reference 11

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Carneiro, António Cunha, and Joaquim Sousa

Reference 12

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Sousa, and António Cunha

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Carneiro, Ana Ferreira, Raul Morais, Joaquim J

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Knowledge-aware Zero-Shot Learning: Survey and Perspective

Reference 16

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This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders A Simple Framework for Contrastive Learning of Visual Representations

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Exploring Simple Siamese Representation Learning

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Improved Baselines with Momentum Contrastive Learning

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Chitwood, Laura L

Reference 20

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Deep Learning with Python

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Cunha, M

Reference 22

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Jubery , Thomas Lübberstedt , and Baskar Ganapathysubramanian

Reference 23

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders De Nart, M

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Imagenet: A large-scale hierarchical image database

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders CrossTransformers: spatially-aware few-shot transfer

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Are Large-scale Datasets Necessary for Self-Supervised Pre-training?

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Sugars, organic acids, and phenolic compounds of ancient grape cultivars ( Vitis vinifera L .) from Igdir province of Eastern Turkey

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Fanzone, Fernando Zamora, Viviana Patricia Jofr \'e , Mariela Assof, Carmen G \'o mez-Cordov \'e s, and \'A lvaro Pe \ n a-Neira

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Big Data

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Ampelography - An old technique with future uses: the case of minor varieties of Vitis vinifera L

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Bootstrap your own latent: A new approach to self-supervised Learning

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Diago, and Javier Tardaguila

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Self-supervised contrastive learning on agricultural images

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders TransFG: A Transformer Architecture for Fine-grained Recognition

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Deep Residual Learning for Image Recognition

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Momentum Contrast for Unsupervised Visual Representation Learning

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Masked Autoencoders Are Scalable Vision Learners

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders INoD : Injected Noise Discriminator for Self - Supervised Representation Learning in Agricultural Fields

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Searching for mobileNetV3

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders An open access repository of images on plant health to enable the development of mobile disease diagnostics

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders DOP Porto

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders The distribution of the world’s grapevine varieties

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders YOLOv5 by Ultralytics , May 2020

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Identification of hickory nuts with different oxidation levels by integrating self-supervised and supervised learning

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Carroll, Craig A

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Fahri Unlersen, Ilker Ali Ozkan, M

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Similarity of Neural Network Representations Revisited

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders ImageNet Classification with Deep Convolutional Neural Networks

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases

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This paper cites Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method

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This paper cites Label-efficient learning in agriculture: A comprehensive review.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Label-efficient learning in agriculture: A comprehensive review

Reference 54

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Research on a Flower Recognition Method Based on Masked Autoencoders

Reference 55

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This paper cites Incremental learning with neural networks for computer vision: a survey.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Incremental learning with neural networks for computer vision: a survey

Reference 56

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This paper cites Self-supervised transformer-based pre-training method using latent semantic masking auto-encoder for pest and disease classification.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Self-supervised transformer-based pre-training method using latent semantic masking auto-encoder for pest and disease classification

Reference 57

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This paper cites Experimental demonstration of Gaussian protocols for one-sided device-independent quantum key distribution.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Experimental demonstration of Gaussian protocols for one-sided device-independent quantum key distribution

Reference 58

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T20:08:11.166084Z

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Observation be104f06-165c-4547-a44b-3d314d95e2b7 · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 59

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This paper cites A ConvNet for the 2020s.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders A ConvNet for the 2020s

Reference 60

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This paper cites A hybrid model of ghost-convolution enlightened transformer for effective diagnosis of grape leaf disease and pest.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders A hybrid model of ghost-convolution enlightened transformer for effective diagnosis of grape leaf disease and pest

Reference 61

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Observation 85347df2-938e-4a5f-92bf-15bd3b85314b · outbound

This paper cites Ogayar, Francisco R.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Ogayar, Francisco R

Reference 62

Resolution: verified exact; doi, observed 2026-08-15T20:08:09.599046Z

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Observation 52ea730b-19a2-489a-977d-f024aee7f3fe · outbound

This paper cites Macklin, Rachel O.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Macklin, Rachel O

Reference 63

Resolution: verified exact; doi, observed 2026-08-15T20:08:09.585417Z

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Observation 5e24cc00-54ec-463f-b1cc-85a1f5e4e2e7 · outbound

This paper cites Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

Reference 64

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This paper cites Strawberry Pests and Diseases Recognition with Self - Supervised Learning.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Strawberry Pests and Diseases Recognition with Self - Supervised Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.346739Z

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This paper cites GVxmi Grapevine Bunches Dataset , October 2022.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders GVxmi Grapevine Bunches Dataset , October 2022

Reference 66

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:10.993241Z

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Observation 975adbae-d85c-4e00-917f-b4aed5af3409 · outbound

This paper cites AI4Agriculture Grape Dataset , November 2021.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders AI4Agriculture Grape Dataset , November 2021

Reference 67

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:10.917769Z

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Unresolved cited work

Reference 68

Resolution: verified exact; doi, observed 2026-08-15T20:08:09.570294Z

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Observation 89264497-0280-400a-9457-eea6d33ac1b5 · outbound

This paper cites Ogidi, Mark G.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Ogidi, Mark G

Reference 69

Resolution: verified exact; doi, observed 2026-08-15T20:08:09.554895Z

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Observation c8c62cfc-11e9-49ae-b9af-421bd91f34bf · outbound

This paper cites SSFE - Net : Self - Supervised Feature Enhancement for Ultra - Fine - Grained Few - Shot Class Incremental Learning.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders SSFE - Net : Self - Supervised Feature Enhancement for Ultra - Fine - Grained Few - Shot Class Incremental Learning

Reference 70

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Observation 0fe2eab5-d31b-49a9-a107-602c456c2445 · outbound

This paper cites Learning from Few Examples: A Summary of Approaches to Few-Shot Learning.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Learning from Few Examples: A Summary of Approaches to Few-Shot Learning

Reference 71

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This paper cites Pavek, Warren F.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Pavek, Warren F

Reference 72

Resolution: verified exact; doi, observed 2026-08-15T20:08:09.540584Z

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Observation ca8452f6-e323-4e7b-aa5c-734248eb939a · outbound

This paper cites Grapevine Bunch Condition Detection Dataset , January 2023.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Grapevine Bunch Condition Detection Dataset , January 2023

Reference 73

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:10.743765Z

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Observation 2b032f65-4167-4cf3-b48a-ce64191c089c · outbound

This paper cites Rankine, John C.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Rankine, John C

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.381668Z

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Observation 4008e07e-242e-4a1d-b642-1772ed6212cc · outbound

This paper cites Robinson, J.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Robinson, J

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.385136Z

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This paper cites Identification and characterization of white grape varieties autochthonous of a warm climate region (andalusia, spain).

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Identification and characterization of white grape varieties autochthonous of a warm climate region (andalusia, spain)

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.388571Z

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Observation 51b5ccc8-c27c-4ba8-916b-b92a99986247 · outbound

This paper cites MobileNetV2 : Inverted Residuals and Linear Bottlenecks.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders MobileNetV2 : Inverted Residuals and Linear Bottlenecks

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.392678Z

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Observation 8de2c505-5c8e-49f9-81e7-f4270495393a · outbound

This paper cites Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD

Reference 78

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:10.612526Z

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Observation 45148428-e882-434f-8e18-7c842f35cdeb · outbound

This paper cites Santos, Leonardo L.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Santos, Leonardo L

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.400213Z

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Observation 8c47dfc3-9e97-4e30-8329-69ab749c68ae · outbound

This paper cites A survey on semi-, self- and unsupervised learning for image classification.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders A survey on semi-, self- and unsupervised learning for image classification

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.403680Z

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Observation 1c654cf4-4580-45f5-85a9-04f70ca7ead7 · outbound

This paper cites Schneider, A.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Schneider, A

Reference 81

Resolution: verified exact; doi, observed 2026-08-15T20:08:09.526501Z

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Observation bd04e526-570a-4cf9-9506-da47930b3f15 · outbound

This paper cites Schmidtke, and Suzy Y.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Schmidtke, and Suzy Y

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.411276Z

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Observation 6ac249c6-945f-42f4-8385-077e8651a9d5 · outbound

This paper cites wGrapeUNIPD - DL : An open dataset for white grape bunch detection.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders wGrapeUNIPD - DL : An open dataset for white grape bunch detection

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.414484Z

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Observation a2ac5750-5618-452d-8fcc-7561ebed1c52 · outbound

This paper cites Going Deeper with Convolutions.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Going Deeper with Convolutions

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.418183Z

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Observation 3f6d143f-7789-4344-80f8-f0e120ab088f · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Rethinking the Inception Architecture for Computer Vision

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.422143Z

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This paper cites Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.426169Z

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This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.429810Z

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Observation 10f5ff84-f2c9-466d-aaee-2fea203b49ee · outbound

This paper cites Automatic detection of grape varieties with the newly proposed CNN model using ampelographic characteristics.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Automatic detection of grape varieties with the newly proposed CNN model using ampelographic characteristics

Reference 88

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T20:08:10.253014Z

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Observation 33cc199f-0167-47a1-b5ac-ae43e28f92bf · outbound

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Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Unresolved cited work

Reference 89

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:10.167695Z

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Observation d8ac4db3-79ec-4937-b8a5-bb2f29b4bf5a · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Training data-efficient image transformers & distillation through attention

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.440572Z

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This paper cites Benchmarking Representation Learning for Natural World Image Collections.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Benchmarking Representation Learning for Natural World Image Collections

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.444247Z

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Observation f7f771d4-b8d7-4f53-83ed-03026cc051da · outbound

This paper cites Grapevine leaves, 2021.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Grapevine leaves, 2021

Reference 92

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:10.067150Z

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Observation 7a8a40a9-a2e0-493e-9aae-c8d2bca3684d · outbound

This paper cites an unresolved cited work.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Unresolved cited work

Reference 93

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:09.990833Z

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Observation 9d993ece-55df-45a4-a7c5-cd606c814f15 · outbound

This paper cites Dense Contrastive Learning for Self-Supervised Visual Pre-Training.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Dense Contrastive Learning for Self-Supervised Visual Pre-Training

Reference 94

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:08:09.934058Z

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Observation 0297f5ed-a367-4224-bd36-2d6a2b07988f · outbound

This paper cites Classification of Plant Leaf Disease Recognition Based on Self - Supervised Learning.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Classification of Plant Leaf Disease Recognition Based on Self - Supervised Learning

Reference 95

Resolution: verified exact; doi, observed 2026-08-15T20:08:09.512016Z

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Observation a60b540d-5054-4a13-aff2-f833621cab5c · outbound

This paper cites Table grape inflorescence dataset, September 2022.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Table grape inflorescence dataset, September 2022

Reference 96

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:09.915451Z

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Observation 0a4bc4be-1510-49f9-b4d7-cd3911a8267f · outbound

This paper cites Self-supervised Animal Detection in Indoor Environment.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Self-supervised Animal Detection in Indoor Environment

Reference 97

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:08:09.843669Z

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Observation df59c39f-8ac3-4598-aba2-a43971990b1b · outbound

This paper cites Barlow Twins: Self-Supervised Learning via Redundancy Reduction.

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:09.476335Z

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