Res2Net Block is a popular image model block that constructs hierarchical residual-like connections within a single residual block. This block has been introduced in Res2Net CNN architecture to represent multi-scale features at a granular level and increase the receptive field for each network layer.
What are Res2Net Blocks?
Res2Net Blocks are image model blocks that construct hierarchical residual-like connections within one single residual block for creating Convolutional Neural Networks (CNNs). These blocks are part of the Res2Net architecture, which focuses on increasing the range of receptive fields for each network layer. Res2Net Blocks represent multi-scale features and expose a new dimension called 'scale' along with the existing dimensions of depth, width, and cardinality.
Working of Res2Net Blocks
The Res2Net Block replaces the traditional $3 * 3$ filters of $n$ channels with a set of smaller filter groups, each having $w$ channels. These smaller filter groups are then connected in a hierarchical residual-like style that increases the number of scales that the output features can represent. The input feature maps are first divided into several groups, and a group of filters extracts features from a specific group of input feature maps. The output features of the previous group are then sent to the next group of filters with another group of input feature maps. This process repeats several times until all input feature maps are processed. Finally, feature maps from all groups are concatenated and sent to another group of $1 * 1$ filters to fuse information together, thus increasing the receptive field.
Advantages of Res2Net Blocks
The Res2Net Blocks enable the creation of CNNs with higher efficiency, accuracy, and speed. They improve multi-scale representation capabilities, increase the range of the receptive field for each layer, and extract more useful features. Additionally, Res2Net Blocks can improve accuracy when dealing with fine-grained visual recognition and object detection problems.
Moreover, the Res2Net architecture can be easily integrated with existing models, and the block is lightweight, which reduces its computations and memory usage. Overall, Res2Net Blocks provide a new way of thinking about CNNs, which demonstrates that adding scale as an additional dimension can help achieve better results in image recognition tasks.
Applications of Res2Net Blocks
Res2Net Blocks have been used in various applications in computer vision, such as image classification, object detection, and semantic segmentation. One notable application of Res2Net Blocks is in the field of natural language processing, where they have been used for character-level representation of text, which leads to high-performance text classification.
Res2Net Blocks are image model blocks that construct hierarchical residual-like connections within a single residual block, and are part of the Res2Net CNN architecture. They represent multi-scale features at a granular level and increase the range of the receptive field for each network layer. Res2Net Blocks can be integrated with existing models to improve their efficiency, accuracy, and speed. They have been successfully applied in various applications in computer vision and natural language processing fields, where they have achieved high-performance results.
