Salient Object Detection

Res2Net is an image model that employs a variation on bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.

A Res2Net Block is an image model block that constructs hierarchical residual-like connections within one single residual block. It was proposed as part of the Res2Net CNN architecture. The block represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The $3 \times 3$ filters of $n$ channels is replaced with a set of smaller filter groups, each with $w$ channels. These smaller filter groups are connected in a hierarchical residual-like style to increase the number of scales that the output features can represent. Specifically, we divide input feature maps into several groups. A group of filters first extracts features from a group of input feature maps. Output features of the previous group are then sent to the next group of filters along 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 $1 \times 1$ filters to fuse information altogether. Along with any possible path in which input features are transformed to output features, the equivalent receptive field increases whenever it passes a $3 \times 3$ filter, resulting in many equivalent feature scales due to combination effects. One way of thinking of these blocks is that they expose a new dimension, scale, alongside the existing dimensions of depth, width, and cardinality.

U2-Net is a two-level nested U-structure architecture that is designed for salient object detection (SOD). The architecture allows the network to go deeper, attain high resolution, without significantly increasing the memory and computation cost. This is achieved by a nested U-structure: on the bottom level, with a novel ReSidual U-block (RSU) module, which is able to extract intra-stage multi-scale features without degrading the feature map resolution; on the top level, there is a U-Net like structure, in which each stage is filled by a RSU block.