Superpixel Partitioning of Very High Resolution Satellite Images for Large-Scale Classification Perspectives with Deep Convolutional Neural Networks

Postadjian, Tristan; Bris, Arnaud Le; Mallet, Clément; Sahbi, Hichem

doi:10.1109/igarss.2018.8519222

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Deep CNNs will be employed here, because they are known to learn more efficiently than shallow CNNs by naturally integrating incredibly enrichened image features 53 . A robust and easily-trainable deep CNN architecture, VGG16, is selected for this study 54 . Regardless of the high performance of VGG16, the complex and spontaneous nature of the boiling bubble dynamics could still require thousands of images per class to learn from the scratch, leading to a substantial cost of data analysis.…”

Section: Resultsmentioning

confidence: 99%

Deep learning predicts boiling heat transfer

Suh

Bostanabad

Won

2021

Sci Rep

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Boiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology.

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Section: Resultsmentioning

confidence: 99%

Deep learning predicts boiling heat transfer

Suh

Bostanabad

Won

2021

Sci Rep

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“…The Feat. stage generally uses a convolutional network, e.g., ResNet [12], RCNN [14], and VGG [22], to extract visual features from the rectified images. The Seq.…”

Section: A Text Recognitionmentioning

confidence: 99%

Towards open-set text recognition via label-to-prototype learning

Liu

Yang²,

Qin³

et al. 2023

Pattern Recognition

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“…The Feat. stage generally uses a convolutional network, e.g., ResNet [12], RCNN [14], and VGG [22], to extract visual features from the rectified images. The Seq.…”

Section: A Text Recognitionmentioning

confidence: 99%

Towards Open-Set Text Recognition via Label-to-Prototype Learning

Liu¹,

Yuan²,

Qin³

et al. 2021

Preprint

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<div><br></div><div>Scene text recognition is a popular topic and can benefit various tasks. Although many methods have been proposed for the close-set text recognition challenges, they cannot be directly applied to open-set scenarios, where the evaluation set contains novel characters not appearing in the training set. Conventional methods require collecting new data and retraining the model to handle these novel characters, which is an expensive and tedious process. In this paper, we propose a label-to-prototype learning framework to handle novel characters without retraining the model. In the proposed framework, novel characters are effectively mapped to their corresponding prototypes with a label-to-prototype learning module. This module is trained on characters with seen labels and can be easily generalized to novel characters. Additionally, feature-level rectification is conducted via topology-preserving transformation, resulting in better alignments between visual features and constructed prototypes while having a reasonably small impact on model speed. A lot of experiments show that our method achieves promising performance on a variety of zero-shot, close-set, and open-set text recognition datasets.</div>

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Superpixel Partitioning of Very High Resolution Satellite Images for Large-Scale Classification Perspectives with Deep Convolutional Neural Networks

Cited by 10 publications

References 10 publications

Deep learning predicts boiling heat transfer

Deep learning predicts boiling heat transfer

Towards open-set text recognition via label-to-prototype learning

Towards Open-Set Text Recognition via Label-to-Prototype Learning

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