Tag
The context module raised accuracy on everyday photos and on street scenes from car cameras. The bigger result was the argument behind it: a network that has to label every pixel can keep its maps large all the way through.
The authors removed VGG-16's last two shrinking steps and beat earlier segmentation networks by more than 5 points. Then they added a small stack of dilated layers, started so each one passes its input through unchanged, and gained more.
A dilated convolution spaces out the positions a filter reads, so the same few weights cover a wider area. Stack a few with growing gaps, and the area each output sees grows exponentially, while the map never shrinks.
Classification networks shrink their feature maps on purpose: it lets later layers see wide areas cheaply. For one label that costs nothing. For a label at every pixel, it throws away the detail the answer needs.
Papers #2 to #4 improved the process of identifying a single thing in an image. Paper #5 labels every pixel instead: road here, pedestrian there, sky above. That needs a network that keeps detail while still seeing a wide area.