Log In Sign Up

Combining the Best of Graphical Models and ConvNets for Semantic Segmentation

by   Michael Cogswell, et al.

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.


page 2

page 6

page 7


ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

Large-scale data is of crucial importance for learning semantic segmenta...

CNN-aware Binary Map for General Semantic Segmentation

In this paper we introduce a novel method for general semantic segmentat...

Learning Statistical Texture for Semantic Segmentation

Existing semantic segmentation works mainly focus on learning the contex...

Specialize and Fuse: Pyramidal Output Representation for Semantic Segmentation

We present a novel pyramidal output representation to ensure parsimony w...

Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency

This paper addresses the problem of semantic part parsing (segmentation)...

Convolutional Feature Masking for Joint Object and Stuff Segmentation

The topic of semantic segmentation has witnessed considerable progress d...

Temporally Distributed Networks for Fast Video Semantic Segmentation

We present TDNet, a temporally distributed network designed for fast and...