Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation | Paper Citation Record

Observation 6f9aa5e2-2195-4dec-bff7-b092b8c10fa3 · outbound

This paper cites Artificial neural network approach for multi- phase segmentation of battery electrode nano-CT images,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Artificial neural network approach for multi- phase segmentation of battery electrode nano-CT images,

Reference 1

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This paper cites Methods—Kintsugi Imaging of Battery Electrodes: Distinguishing Pores from the Carbon Binder Domain using Pt Deposition,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Methods—Kintsugi Imaging of Battery Electrodes: Distinguishing Pores from the Carbon Binder Domain using Pt Deposition,

Reference 3

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This paper cites Au- tomated segmentation of large image datasets using artificial intelligence for microstructure characterisation and damage analysis,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Au- tomated segmentation of large image datasets using artificial intelligence for microstructure characterisation and damage analysis,

Reference 5

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This paper cites Guiding the Design of Heteroge- neous Electrode Microstructures for Li-Ion Bat- teries: Microscopic Imaging, Predictive Model- ing, and Machine Learning,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Guiding the Design of Heteroge- neous Electrode Microstructures for Li-Ion Bat- teries: Microscopic Imaging, Predictive Model- ing, and Machine Learning,

Reference 6

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Observation e399500f-e483-435f-9935-42d2e9e1fc1b · outbound

This paper cites Microstructure segmentation with deep learn- ing encoders pre-trained on a large microscopy dataset,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Microstructure segmentation with deep learn- ing encoders pre-trained on a large microscopy dataset,

Reference 7

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This paper cites Quantification and modeling of mechanical degradation in lithium-ion batteries based on nanoscale imaging,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Quantification and modeling of mechanical degradation in lithium-ion batteries based on nanoscale imaging,

Reference 8

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This paper cites TauFactor: An open-source appli- 11 Docherty et al. Feature Upsampling & Micrograph Segmentation Preprint cation for calculating tortuosity factors from to- mographic data,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation TauFactor: An open-source appli- 11 Docherty et al. Feature Upsampling & Micrograph Segmentation Preprint cation for calculating tortuosity factors from to- mographic data,

Reference 9

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This paper cites Taufactor 2: A gpu accelerated python tool for microstruc- tural analysis,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Taufactor 2: A gpu accelerated python tool for microstruc- tural analysis,

Reference 10

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Observation 4b330177-140a-4be6-8e4c-84d57bd016c7 · outbound

This paper cites X-ray computed tomography,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation X-ray computed tomography,

Reference 11

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Observation cd046449-b5f7-46e1-ad2a-0c53cd605f32 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 12

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This paper cites Graph-constrained Contrastive Regularization for Semi-weakly V olumetric Seg- mentation,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Graph-constrained Contrastive Regularization for Semi-weakly V olumetric Seg- mentation,

Reference 13

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This paper cites Segment Anything.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Segment Anything

Reference 14

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This paper cites SAMBA: A Trainable Segmenta- tion Web-App with Smart Labelling,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation SAMBA: A Trainable Segmenta- tion Web-App with Smart Labelling,

Reference 15

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This paper cites Cellpose 2.0: how to train your own model,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Cellpose 2.0: how to train your own model,

Reference 16

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Observation 36b82d75-ad0f-4db3-8364-46fd17040bcb · outbound

This paper cites A Threshold Selection Method from Gray-Level Histograms,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation A Threshold Selection Method from Gray-Level Histograms,

Reference 17

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This paper cites Algorithm as 136: A k-means clustering algorithm,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Algorithm as 136: A k-means clustering algorithm,

Reference 18

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Observation e2934b59-d9aa-432a-a648-06b0c23e450b · outbound

This paper cites Topographic distance and watershed lines,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Topographic distance and watershed lines,

Reference 19

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Observation fbd0381b-442e-48d9-ab2d-d38b255d416d · outbound

This paper cites A Generalization of Otsu's Method and Minimum Error Thresholding.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation A Generalization of Otsu's Method and Minimum Error Thresholding

Reference 20

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Observation 2d8f8bae-1e19-4420-9066-714b0dda8856 · outbound

This paper cites Resolving the Discrep- ancy in Tortuosity Factor Estimation for Li-Ion Battery Electrodes through Micro-Macro Mod- eling and Experiment,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Resolving the Discrep- ancy in Tortuosity Factor Estimation for Li-Ion Battery Electrodes through Micro-Macro Mod- eling and Experiment,

Reference 21

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This paper cites Trainable Weka Segmen- tation: a machine learning tool for microscopy pixel classification,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Trainable Weka Segmen- tation: a machine learning tool for microscopy pixel classification,

Reference 22

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This paper cites ilastik: interactive machine learn- ing for (bio)image analysis,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation ilastik: interactive machine learn- ing for (bio)image analysis,

Reference 23

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Observation 35967d0c-de02-40d2-9319-ae771e0f20a3 · outbound

This paper cites Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation

Reference 24

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This paper cites Auto-Context and Its Ap- plication to High-Level Vision Tasks and 3D Brain Image Segmentation,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Auto-Context and Its Ap- plication to High-Level Vision Tasks and 3D Brain Image Segmentation,

Reference 25

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This paper cites ExpertSegmentation: Segmentation for mi- croscopy with domain-informed targets via cus- tom loss,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation ExpertSegmentation: Segmentation for mi- croscopy with domain-informed targets via cus- tom loss,

Reference 26

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Observation 29b77fe9-18d2-432a-b706-b2f94eb68ccb · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Emerging Properties in Self-Supervised Vision Transformers

Reference 27

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation DINOv2: Learning Robust Visual Features without Supervision

Reference 28

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This paper cites Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Reference 29

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This paper cites Deep ViT Features as Dense Visual Descriptors.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Deep ViT Features as Dense Visual Descriptors

Reference 30

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This paper cites FeatUp: A Model-Agnostic Framework for Features at Any Resolution.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation FeatUp: A Model-Agnostic Framework for Features at Any Resolution

Reference 31

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This paper cites LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation LiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors

Reference 32

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This paper cites LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models

Reference 33

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This paper cites haesleinhuepf/napari-accelerated-pixel-and- object- classification: 0.14.1,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation haesleinhuepf/napari-accelerated-pixel-and- object- classification: 0.14.1,

Reference 34

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This paper cites Measuring the Particle Packing of l-Glutamic Acid Crystals through X-ray Computed To- mography for Understanding Powder Flow and Consolidation Behavior,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Measuring the Particle Packing of l-Glutamic Acid Crystals through X-ray Computed To- mography for Understanding Powder Flow and Consolidation Behavior,

Reference 35

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This paper cites Attention is all you need,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Attention is all you need,

Reference 36

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

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 37

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This paper cites Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Reference 38

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This paper cites Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Reference 39

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This paper cites A Cookbook of Self-Supervised Learning.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation A Cookbook of Self-Supervised Learning

Reference 40

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This paper cites Masked Autoencoders Are Scalable Vision Learners.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Masked Autoencoders Are Scalable Vision Learners

Reference 41

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This paper cites NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Reference 42

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This paper cites Vision Transformers Need Registers.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Vision Transformers Need Registers

Reference 43

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This paper cites Improving 2D Feature Representations by 3D-Aware Fine-Tuning.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Improving 2D Feature Representations by 3D-Aware Fine-Tuning

Reference 44

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This paper cites ImageNet Large Scale Visual Recognition Challenge.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation ImageNet Large Scale Visual Recognition Challenge

Reference 45

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This paper cites Random Forests,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Random Forests,

Reference 46

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This paper cites Xgboost: A scal- able tree boosting system,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Xgboost: A scal- able tree boosting system,

Reference 47

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This paper cites Biphase cathode sem.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Biphase cathode sem

Reference 48

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This paper cites Electron micrographs.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Electron micrographs

Reference 49

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Observation 2d899abd-8a4c-4176-90db-cfd532b0622c · outbound

This paper cites Semi-automatic deter- mination of cell surface areas used in systems biology.,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Semi-automatic deter- mination of cell surface areas used in systems biology.,

Reference 50

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This paper cites an unresolved cited work.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Unresolved cited work

Reference 51

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This paper cites Deep Residual Learning for Image Recognition.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Deep Residual Learning for Image Recognition

Reference 52

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Observation ade0db72-fab0-4679-8e94-cf9aa4052f8b · outbound

This paper cites Utilizing active learning to accel- erate segmentation of microstructures with tiny annotation budgets,.

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Utilizing active learning to accel- erate segmentation of microstructures with tiny annotation budgets,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:44:21.804218Z

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Reference 54

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Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation scikit-image: image processing in python,

Reference 55

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Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Heterogeneity of the Dominant Causes of Performance Loss in End-of-Life Cathodes and Their Consequences for Direct Recycling,

Reference 56

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Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation

Reference 57

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Reference 58

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Reference 59

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Reference 61

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Reference 62

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Reference 66

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Reference 68

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Reference 69

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Reference 2017

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