Review — Scene Parsing through ADE20K Dataset (Semantic Segmentation)

Cascade-SegNet & Cascade-DilatedNet is Formed Using Cascade Segmentation Module, Outperforms DilatedNet, SegNet & FCN

ADE20K Dataset (The first row shows the sample images, the second row shows the annotation of objects and stuff, and the third row shows the annotation of object parts.)

In this story, Scene Parsing through ADE20K Dataset, (Cascade-SegNet & Cascade-DilatedNet), by Massachusetts Institute of Technology, and University of Toronto, is briefly reviewed. In this paper:

  • Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines.
  • This module is integrated with SegNet and DilatedNet to form the Cascade-SegNet and Cascade-DilatedNet respectively.

This is a paper in 2017 CVPR with over 1000 citations. (Sik-Ho Tsang @ Medium)

Outline

  1. Cascade Segmentation Module
  2. Experimental Results

1. Cascade Segmentation Module

Cascade Segmentation Module
  • While the frequency of objects appearing in scenes follows a long-tail distribution, the pixel ratios of objects also follow such a distribution.

For example, the stuff classes like ‘wall’, ‘building’, ‘floor’, and ‘sky’ occupy more than 40% of all the annotated pixels, while the discrete objects, such as ‘vase’ and ‘microwave’ at the tail of the distribution, occupy only 0.03% of the annotated pixels.

Because of the long-tail distribution, a semantic segmentation network can be easily dominated by the most frequent stuff classes.

  • Semantic classes of the scenes into three macro classes: stuff (sky, road, building, etc), foreground objects (car, tree, sofa, etc), and object parts (car wheels and door, people head and torso, etc).
  • Different streams of high-level layers are used to represent different macro classes and recognize the assigned classes, as shown above.
  • More specifically, the stuff stream is trained to classify all the stuff classes plus one foreground object class.
  • The object stream is trained to classify the discrete objects.
  • The part stream further segments parts on each object score map predicted from the object stream.
  • Each stream has a training loss at the end.
  • The network with the two streams (stuff+objects) or three streams (stuff+objects+parts) could be trained end-to-end:
  • The streams share the weights of the lower layers.
  • This proposed module is integrated on two baseline networks SegNet and DilatedNet.

2. Experimental Results

2.1. Objective Evaluation

Performance on the validation set of SceneParse150
Performance of stuff and discrete object segmentation
  • The top 150 objects ranked by their total pixel ratios are selected from the ADE20K dataset and used to build a scene parsing benchmark of ADE20K, termed as MIT SceneParse150.
  • Among the baselines, the DilatedNet achieves the best performance on the SceneParse150.
  • The cascade networks, Cascade-SegNet and Cascade-DilatedNet both outperform the original baselines.

2.2. Visualization

Ground-truths, segmentation results given by the networks, and objectness maps given by the Cascade-DilatedNet
  • Some examples of segmentation results are visualized above.

2.3. Possible Applications

Applications of scene parsing
  • (a) Automatic image content removal using the object score maps predicted by the scene parsing network.
  • (b) Scene synthesis.

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