Semantic Segmentation - DeepLab V3+

Semantic Segmentation - DeepLab V3+

Semantic Segmentation

Semantic segmentation involves partitioning/marking regions in the image
belonging to different objects/classes. Deep learning methods have made a remarkable
improvement in this field within the past few years. This short article summarises
DeepLab V3+, an elegant extension of DeepLab v3 proposed by the same authors (Chen et al.).

Examples of semantic segmentation. Source


(a) ASPP style architecture (b) Encoder Decoder style architecture (c) Proposed architecture. Source

Previously, ASPP (Atrous Spatial Pyramid Pooling) has been used to extract rich multi-scale features from images. The authors of Deeplab v3+ try to combine the ASPP module with the good old encoder-decoder architecture with skip connections, thereby providing better details in predictions.


DeepLab v3+ architecture. Source

Here are the key features of this architecture:

  • Atrous Depthwise Convolution: The depthwise convolution has an added dilation to make it atrous.
  • ASPP style encoder from DeepLab V3 + UNet style decoder with skip connections.
  • Modified Xception network as the backbone: This can be replaced by any backbone; HRNet seems to be widely used these days.


Results show different backbones with Bilinear Upsampling(BU) vs a decoder. Source


  1. ( Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
  2. ( Xception: Deep Learning with Depthwise Separable Convolutions
  3. ( DeepLab: Semantic Image Segmentation with
    Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
  4. ( MobileNetV2: Inverted Residuals and Linear Bottlenecks