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 layerwise 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 will be made publicly available.
Res2Net REF constructs hierarchical residual-like connections within each single residual block to generate multiscale feature maps.
Res2Net: A New Multi-scale Backbone Architecture
IEEE TPAMI 2020
Medicine Computer Science