Method Details

Details for method 'OCNet_ResNet101_fine'

name	OCNet_ResNet101_fine
challenge	pixel-level semantic labeling
details	Context is essential for various computer vision tasks. The state-of-the-art scene parsing methods define the context as the prior of the scene categories (e.g., bathroom, badroom, street). Such scene context is not suitable for the street scene parsing tasks as most of the scenes are similar. In this work, we propose the Object Context that captures the prior of the object's category that the pixel belongs to. We compute the object context by aggregating all the pixels' features according to a attention map that encodes the probability of each pixel that it belongs to the same category with the associated pixel. Specifically, We employ the self-attention method to compute the pixel-wise attention map. We further propose the Pyramid Object Context and Atrous Spatial Pyramid Object Context to handle the problem of multi-scales.
publication	Anonymous
project page / code
used Cityscapes data	fine annotations
used external data	ImageNet
runtime	n/a
subsampling	no
submission date	August, 2018
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