Combining the Best of Graphical Models and ConvNets for Semantic Segmentation
By Michael Cogswell, Xiao Lin, Senthil Purushwalkam, Dhruv Batra. 2014
16 citations recorded by the source corpus.
We present a two-module approach to semantic segmentation that incorporates Convolutional Networks (CNNs) and Graphical Models. Graphical models are used to generate a small (5-30) set of diverse segmentations proposals, such that this set has high recall. Since the number of required proposals is so low, we can extract fairly complex features to rank them. Our complex feature of choice is a novel CNN called SegNet, which directly outputs a (coarse) semantic segmentation. Importantly, SegNet is specifically trained to optimize the corpus-level PASCAL IOU loss function. To the best of our knowledge, this is the first CNN specifically designed for semantic segmentation. This two-module approach achieves $52.5\%$ on the PASCAL 2012 segmentation challenge.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
- Chunhua Shen
- Luc Van Gool
- Alan L. Yuille
- Dacheng Tao
- Xiaogang Wang
- Ming-Hsuan Yang
- Bernt Schiele
- Trevor Darrell
Citations in the corpus
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs (2016, methodology): “Specifically, [53] use CRFs as a proposal mechanism for a DCNN-based reranking system, while [39] treat superpixels as nodes for a local pairwise CRF and use graph-cuts for discrete inference.”
- Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs (2014, methodology): “Focusing on the closest works in this direction, Cogswell et al. (2014) use CRFs as a proposal mechanism for a DCNN-based reranking system, while Farabet et al. (2013) treat superpixels as nodes for a local pairwise CRF and use graph-cuts for discrete inference; as such their results can be limited…”
- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields (2015, methodology): “In the following, we give a brief introduction to the most closely related work.”