Semi-Supervised Instance Segmentation

A Spatial Attention-Guided Mask is a module for instance segmentation that predicts a segmentation mask on each detected box with a spatial attention map that helps to focus on informative pixels and suppress noise. The goal is to guide the mask head for spotlighting meaningful pixels and repressing uninformative ones. Once features inside the predicted RoIs are extracted by RoIAlign with 14×14 resolution, those features are fed into four conv layers and the spatial attention module (SAM) sequentially. To exploit the spatial attention map $A\_{sag}\left(X\_{i}\right) \in \mathcal{R}^{1\times{W}\times{H}}$ as a feature descriptor given input feature map $X\_{i} \in \mathcal{R}^{C×W×H}$, the SAM first generates pooled features $P\_{avg}, P\_{max} \in \mathcal{R}^{1\times{W}\times{H}}$ by both average and max pooling operations respectively along the channel axis and aggregates them via concatenation. Then it is followed by a 3 × 3 conv layer and normalized by the sigmoid function. The computation process is summarized as follow: $$ A\_{sag}\left(X\_{i}\right) = \sigma\left(F\_{3\times{3}}(P\_{max} \cdot P\_{avg})\right) $$ where $\sigma$ denotes the sigmoid function, $F\_{3\times{3}}$ is 3 × 3 conv layer and $\cdot$ represents the concatenate operation. Finally, the attention guided feature map $X\_{sag} ∈ \mathcal{R}^{C\times{W}\times{H}}$ is computed as: $$ X\_{sag} = A\_{sag}\left(X\_{i}\right) \otimes X\_{i} $$ where ⊗ denotes element-wise multiplication. After then, a 2 × 2 deconv upsamples the spatially attended feature map to 28 × 28 resolution. Lastly, a 1 × 1 conv is applied for predicting class-specific masks.

One-Shot Aggregation with an Identity Mapping and eSE is an image model block that extends one-shot aggregation with a residual connection and effective squeeze-and-excitation block. It is proposed as part of the VoVNetV2 CNN architecture. The module adds an identity mapping to the OSA module - the input path is connected to the end of an OSA module that is able to backpropagate the gradients of every OSA module in an end-to-end manner on each stage like a ResNet. Additionally, a channel attention module - effective Squeeze-Excitation - is used which is like regular squeeze-and-excitation but uses only one FC layer with $C$ channels instead of two FCs without a channel dimension reduction, which maintains channel information.

VoVNetV2 is a convolutional neural network that improves upon VoVNet with two effective strategies: (1) residual connection for alleviating the optimization problem of larger VoVNets and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of the original squeeze-and-excitation module.