Res2Net: A New Multi-Scale Backbone Architecture

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. Ho...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 43; H. 2; S. 652 - 662
Hauptverfasser:	Gao, Shang-Hua, Cheng, Ming-Ming, Zhao, Kai, Zhang, Xin-Yu, Yang, Ming-Hsuan, Torr, Philip
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Ablation Artificial neural networks Backbone Computer architecture Computer vision Convolution deep learning Feature extraction Kernel Multi-scale Object detection Object recognition Salience Semantics Source code Task analysis
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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Abstract	Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.
AbstractList	Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/. Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/ .
Author	Zhao, Kai Yang, Ming-Hsuan Torr, Philip Gao, Shang-Hua Cheng, Ming-Ming Zhang, Xin-Yu
Author_xml	– sequence: 1 givenname: Shang-Hua orcidid: 0000-0002-7055-2703 surname: Gao fullname: Gao, Shang-Hua email: shgao@mail.nankai.edu.cn organization: TKLNDST, College of Computer Science, Nankai University, Tianjin, China – sequence: 2 givenname: Ming-Ming orcidid: 0000-0001-5550-8758 surname: Cheng fullname: Cheng, Ming-Ming email: cmm@nankai.edu.cn organization: TKLNDST, College of Computer Science, Nankai University, Tianjin, China – sequence: 3 givenname: Kai orcidid: 0000-0002-2496-0829 surname: Zhao fullname: Zhao, Kai email: kaiz.xyz@gmail.com organization: TKLNDST, College of Computer Science, Nankai University, Tianjin, China – sequence: 4 givenname: Xin-Yu orcidid: 0000-0002-4335-682X surname: Zhang fullname: Zhang, Xin-Yu email: xinyuzhang@mail.nankai.edu.cn organization: TKLNDST, College of Computer Science, Nankai University, Tianjin, China – sequence: 5 givenname: Ming-Hsuan orcidid: 0000-0003-4848-2304 surname: Yang fullname: Yang, Ming-Hsuan email: mhyang@ucmerced.edu organization: UC Merced, Merced, CA, USA – sequence: 6 givenname: Philip surname: Torr fullname: Torr, Philip email: philip.torr@eng.ox.ac.uk organization: Oxford University, Oxford, United Kingdom
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31484108$$D View this record in MEDLINE/PubMed
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Snippet	Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs)...
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SubjectTerms	Ablation Artificial neural networks Backbone Computer architecture Computer vision Convolution deep learning Feature extraction Kernel Multi-scale Object detection Object recognition Salience Semantics Source code Task analysis
Title	Res2Net: A New Multi-Scale Backbone Architecture
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