Blogs
A 2020 guide to Semantic Segmentation [Link]
How to do Semantic Segmentation using Deep learning [Link]
Semantic Segmentation: Introduction to the Deep Learning Technique Behind Google Pixel’s Camera! [Link]
Image Segmentation in 2020: Architectures, Losses, Datasets, and Frameworks [Link]
Semantic Segmentation on Cityscapes test [Link]
Research Papers
Zhang, Hang, et al. “ResNeSt: Split-Attention Networks.” ArXiv:2004.08955 [Cs], Apr. 2020. arXiv.org, https://arxiv.org/pdf/2004.08955v1.pdf
Cheng, Bowen, et al. “Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation.” ArXiv:1911.10194 [Cs], Mar. 2020. arXiv.org, https://arxiv.org/pdf/1911.10194v3.pdf
Wang, Jingdong, et al. “Deep High-Resolution Representation Learning for Visual Recognition.” ArXiv:1908.07919 [Cs], Mar. 2020. arXiv.org, https://arxiv.org/pdf/1908.07919v2.pdf
Yuan, Yuhui, et al. “Object-Contextual Representations for Semantic Segmentation.” ArXiv:1909.11065 [Cs], July 2020. arXiv.org, https://arxiv.org/pdf/1909.11065v5.pdf
Chen, Liang-Chieh, et al. “Rethinking Atrous Convolution for Semantic Image Segmentation.” ArXiv:1706.05587 [Cs], Dec. 2017. arXiv.org, https://arxiv.org/pdf/1706.05587v3.pdf