Investigating the Feature Collection for Semantic Segmentation via Single Skip Connection

Investigating the feature collection for semantic segmentation via single skip connection

Since the study of deep convolutional neural network became prevalent, one of the important discoveries is that a feature map from a convolutional network can be extracted before going into the fully connected layer and can be used as a saliency map for object detection. Furthermore, the model can use features from each different layer for accurate object detection: the features from different ...

Multiple Skip Connections and Dilated Convolutions for Semantic Segmentation

We propose a scale-aware semantic segmentation method specifically for small objects. The contributions of this method are 1) to feed the features of a small region by using multiple skip connections, and 2) to extract context from multiple receptive fields by using multiple dilated convolution blocks. The proposed method has achieved high accuracy in the Cityscapes dataset. In comparison with ...

Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation

Recent deep CNNs contain forward shortcut connections; i.e. skip connections from low to high layers. Reusing features from lower layers that have higher resolution (location information) benefit higher layers to recover lost details and mitigate information degradation. However, during inference the lower layers do not know about high layer features, although they contain contextual high seman...

Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques

Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before softwa...

Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to segmentation of white matter lesions