  Convolutional Neural Networks (CNN) has been widely applied in the realm of
computer vision. However, given the fact that CNN models are translation
invariant, they are not aware of the coordinate information of each pixel. Thus
the generalization ability of CNN will be limited since the coordinate
information is crucial for a model to learn affine transformations which
directly operate on the coordinate of each pixel. In this project, we proposed
a simple approach to incorporate the coordinate information to the CNN model
through coordinate embedding. Our approach does not change the downstream model
architecture and can be easily applied to the pre-trained models for the task
like object detection. Our experiments on the German Traffic Sign Detection
Benchmark show that our approach not only significantly improve the model
performance but also have better robustness with respect to the affine
transformation.
