[논문 정리] What Makes Training Multi-modal Classification Networks Hard?

[논문 정리] What Makes Training Multi-modal Classification Networks Hard?

논문정보 What Makes Training Multi-modal Classification Networks Hard? What Makes Training Multi-Modal Classification Networks Hard? Consider end-to-end training of a multi-modal vs. a single-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its single-modal counterpart. In our experiments, however arxiv.org - ..

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  • · 2023. 2. 7.
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[논문 정리] HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification

[논문 정리] HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification

논문정보 HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification Medical image classification has developed rapidly under the impetus of the convolutional neural network (CNN). Due to the fixed size of the receptive field of the convolution kernel, it is difficult to capture the ..

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  • · 2023. 2. 3.
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[논문 정리] MetaFormer : A Unified Meta Framework for Fine-Grained Recognition

[논문 정리] MetaFormer : A Unified Meta Framework for Fine-Grained Recognition

논문정보 MetaFormer : A Unified Meta Framework for Fine-Grained Recognition MetaFormer: A Unified Meta Framework for Fine-Grained Recognition Fine-Grained Visual Classification(FGVC) is the task that requires recognizing the objects belonging to multiple subordinate categories of a super-category. Recent state-of-the-art methods usually design sophisticated learning pipelines to tackle this task arx..

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  • · 2023. 1. 23.
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[논문 정리] ResNeSt : Split-Attention Networks

[논문 정리] ResNeSt : Split-Attention Networks

논문정보 ResNeSt : Split-Attention Networks ResNeSt: Split-Attention Networks It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their su arxiv.org GitHub - zhanghang1989/ResNeSt: ResNeSt: Split-Attention ..

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  • · 2023. 1. 6.
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[논문 정리] Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

[논문 정리] Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

논문정보 Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is no..

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  • · 2023. 1. 1.
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[논문 정리] Voting in Transfer Learning System for Ground-Based Cloud Classification

[논문 정리] Voting in Transfer Learning System for Ground-Based Cloud Classification

논문정보 Voting in Transfer Learning System for Ground-Based Cloud Classification Voting in Transfer Learning System for Ground-Based Cloud Classification Cloud classification is a great challenge in meteorological research. The different types of clouds, currently known and present in our skies, can produce radioactive effects that impact the variation of atmospheric conditions, with consequent str..

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  • · 2022. 12. 29.
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