We find that the complexity of our model can be reduced by using the same CNN with shared weights for both image and part patches, but the zero-shot learning performance is slightly degraded, e.g., the score for CUB-PS and AWA-PS decreases by 3.6%, 2.7%, respectively.
the heavy involvement of human labor for part annotations makes tasks costly in the real large-scale problems.