SRM : A Style-based Recalibration Module for Convolutional Neural Networks

Lee, HyunJae; Kim, Hyoeun; Nam, Hyeonseob

doi:10.48550/arxiv.1903.10829

Cited by 2 publications

References 21 publications

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Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector

Wang

et al. 2020

Pattern Recognition Letters

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Different layers of deep convolutional neural networks(CNNs) can encode different-level information. Highlayer features always contain more semantic information, and low-layer features contain more detail information. However, low-layer features suffer from the background clutter and semantic ambiguity. During visual recognition, the feature combination of the low-layer and high-level features plays an important role in context modulation. If directly combining the high-layer and low-layer features, the background clutter and semantic ambiguity may be caused due to the introduction of detailed information. In this paper, we propose a general network architecture to concatenate CNN features of different layers in a simple and effective way, called Selective Feature Connection Mechanism (SFCM). Low-level features are selectively linked to highlevel features with a feature selector which is generated by high-level features. The proposed connection mechanism can effectively overcome the above-mentioned drawbacks. We demonstrate the effectiveness, superiority, and universal applicability of this method on multiple challenging computer vision tasks, including image classification, scene text detection, and image-to-image translation.

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Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector

Wang

et al. 2020

Pattern Recognition Letters

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Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey

Le¹,

Rathour²,

Yamazaki³

et al. 2021

Preprint

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Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision. In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision. We start with comprehending the theories of deep learning, reinforcement learning, and deep reinforcement learning. We then propose a categorization of deep reinforcement learning methodologies and discuss their advantages and limitations. In particular, we divide deep reinforcement learning into seven main categories according to their applications in computer vision, i.e. (i) landmark localization (ii) object detection; (iii) object tracking; (iv) registration on both 2D image and 3D image volumetric data (v) image segmentation; (vi) videos analysis; and (vii) other applications. Each of these categories is further analyzed with reinforcement learning techniques, network design, and performance. Moreover, we provide a comprehensive analysis of the existing publicly available datasets and examine source code availability. Finally, we present some open issues and discuss future research directions on deep reinforcement learning in computer vision.

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SRM : A Style-based Recalibration Module for Convolutional Neural Networks

Cited by 2 publications

References 21 publications

Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector

Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector

Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey

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