# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 2 Related Work
[18], applies channel-wise attention to different network branches to learn diverse feature representations and cross-feature information. [PERSON] et al. [19] proposed Hierarchical Parsing Net for semantic segmentation which enhances scene parsing by learning the global scene information and the contextual relation between objects in the scene using a deep neural network. [PERSON] et al. [20] proposed a one-shot semantic segmentation method which uses the multiclass labels information during training to encourage the network to learn more accurate semantic features of each category and they also proposed the pyramid feature fusion module to mine the fused features of the objects and a self-prototype guidance branch to support the segmentation task. Although all the previously mentioned methods presented challenging results, they adopted inefficient decoding stages that eliminate the object details and introduce some noise. [PERSON] et al. [21] proposed pre-training and self-training techniques using stronger augmentation on ImageNet [22] across the different image sizes using EfficientNet [23] architecture, and showed that pre-training and self-training are mutually beneficial and improve the accuracies of both the semantic segmentation and object detection tasks. Further, [PERSON] et al. [24] proposed dilated SpineNet or SpineNet-Seg, which is a neural architecture search discovered network from DeepLabv3. In this approach, the scale permuted networks originally used for object detection in the semantic segmentation task are evaluated, adopting a customized dilation ratio per block. [PERSON] et al. [25] proposed the multiscale deep equilibrium model (MDEQ), MDEQ backpropagates through the equilibrium points of multiple scale features simultaneously using simple differentiation to avoid storing intermediate states, they attained a high segmentation accuracy on CITYSCAPES however, their model has high computational complexity. [PERSON] et al. [26] proposed EEEA-Net in which they employed a neural architecture search method to search an optimized model with the lowest number of parameters by using an early exit population initialization algorithm. They could achieve an average accuracy segmentation model but the number of the parameters of their model was extremely low. [PERSON] et al. [27] recently proposed RepVGG in which they adopted a VGG-like architecture composed of a stack of \(3\times 3\) convolution and Relu. RepVGG models ran much faster than ResNet-50 or ResNet-101 with higher accuracy on the classification and semantic segmentation tasks. It is important to note that [PERSON] et al. [28] initiated the direction of using depth-to-space for segmentation when they employed it to perform binary segmentation for satellite maps in DeepGlobe dataset [29] using ResNet and VGG16 backbones but their implementation was not efficient enough and our model can produce better quality segmentation than their model. Other methods [30;31] employed depth information to support the semantic segmentation task. [PERSON] et al. [30] proposed a depth adaptive deep neural network for semantic segmentation using a depth-adaptive multiscale convolutional layer consisting of the adaptive perception neuron and the in-layer multiscale neuron to adjust the receptive field at each spatial location and to apply the different size of the receptive field at each feature space to learn features at multiple scales, respectively. [PERSON] et al. [31] proposed Hard Pixel Mining for semantic segmentation using a multiscale loss weight map generated by the depth data to enforce the model to pay more attention to the hard pixels in segmentation. They employed the depth data during the training step only and did not use it in the testing step. Other studies have shown the ability of CNN models to perform the joint task of semantic segmentation and depth estimation. For example, [PERSON] et al. [32] proposed a multi-scale fully convolutional CNN for simultaneous semantic segmentation and monocular depth estimation.