I was reading the well know paper Fully Convolutional Networks for Semantic Segmentation, and, throughout the whole paper, they talk use the term fine and coarse. I was wondering what they mean. The first time they say it in the intro is:
Convolutional networks are driving advances in recognition. Convnets are not only improving for whole-image classification, but also making progress on local tasks with structured output. These include advances in bounding box object detection, part and keypoint prediction, and local correspondence.
The natural next step in the progression from coarse to fine inference is to make a prediction at every pixel.
It's also used in other parts of the paper
We next explain how to convert classification nets into fully convolutional nets that produce coarse output maps.
What do "coarse" and "fine" mean in the context of this paper? And in the general context of computer vision?
In English, "coarse" means "rough or loose in texture or grain" , while "fine" means "involving great attention to detail" or "(chiefly of wood) having a fine or delicate arrangement of fibers", but these definitions do not elucidate the meaning of these words in the context of computer vision.
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