Modeling long range relations by feature translation

Long range relations play a key role in tasks like human pose estimation that requires dense prediction. We propose an additional module containing a process called feature translation, to gather long range information at early stages. It is shown that such module has connection with dilated convolution and is more efficient. The module significantly improves performance in pose estimation and we show that most of the improvement is contributed by the feature translation process.