[논문 정리] Hierarchical Multimodal Fusion for Ground Based Cloud Classification in Weather Station Networks

Hierarchical Multimodal Fusion for Ground Based Cloud Classification in Weather Station Networks Hierarchical Multimodal Fusion for Ground-Based Cloud Classification in Weather Station Networks Recently, the multimodal information is taken into consideration for ground-based cloud classification in weather station networks, but intrinsic correlations between the multimodal information and the vi..

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• · 2022. 12. 28.
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[논문 정리] Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network

논문정보 Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similari..

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• · 2022. 12. 27.
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[논문 정리] ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

논문정보 ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention mod..

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• · 2022. 12. 24.
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[논문 정리] An image is worth 16x16 words : Transformers for image recognition

논문정보 An image is worth 16x16 words :Transformers for image recognition An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to..

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• · 2022. 12. 17.
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[논문정리] TransFG : A Transformer Architecture for Fine-Grained Recognition

논문정보 TransFG : A Transformer Architecture for Fine-Grained Recognition TransFG: A Transformer Architecture for Fine-grained Recognition Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences. Most existing works mainly tackle this problem by reusing the backbone network t arxiv..

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• · 2022. 11. 10.
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[논문정리] A Novel Plug-in Module for Fine-Grained Visual Classification

논문정보 A Novel Plug-in Module for Fine-Grained Visual Classification A Novel Plug-in Module for Fine-Grained Visual Classification Visual classification can be divided into coarse-grained and fine-grained classification. Coarse-grained classification represents categories with a large degree of dissimilarity, such as the classification of cats and dogs, while fine-grained classificati arxiv.org 논문..

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• · 2022. 10. 20.
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