Semantic Segmentation: A Zoology of Deep Architectures

In this paper we review the evolution of deep architectures for semantic segmentation. The (cid:28)rst successful model was fully convolutional network (FCN) published in CVPR in 2015. Since then, the subject has become very popular and many methods have been published, mainly proposing improvements of FCN. We describe in detail the Pyramid Scene Parsing Network (PSPnet) and DeepLabV3, in addition to FCN, which provide a multi-scale description and increase the resolution of segmentation. In recent years, convolutional architectures have reached a bottleneck and have been surpassed by transformers from natural language processing (NLP), even though these models are generally larger and slower. We have chosen to discuss about the Segmentation Transformer (SETR), a (cid:28)rst architecture with a transformer backbone. We also discuss SegFormer, that includes a multi-scale interpretation and tricks to decrease the size and inference time of the network. The networks presented in the demo come from the MM-Segmentation library, an open source semantic segmentation toolbox based on PyTorch. We propose to compare these methods qualitatively