  As powerful as fine-grained visual classification (FGVC) is, responding your
query with a bird name of "Whip-poor-will" or "Mallard" probably does not make
much sense. This however commonly accepted in the literature, underlines a
fundamental question interfacing AI and human -- what constitutes transferable
knowledge for human to learn from AI? This paper sets out to answer this very
question using FGVC as a test bed. Specifically, we envisage a scenario where a
trained FGVC model (the AI expert) functions as a knowledge provider in
enabling average people (you and me) to become better domain experts ourselves,
i.e. those capable in distinguishing between "Whip-poor-will" and "Mallard".
Fig. 1 lays out our approach in answering this question. Assuming an AI expert
trained using expert human labels, we ask (i) what is the best transferable
knowledge we can extract from AI, and (ii) what is the most practical means to
measure the gains in expertise given that knowledge? On the former, we propose
to represent knowledge as highly discriminative visual regions that are
expert-exclusive. For that, we devise a multi-stage learning framework, which
starts with modelling visual attention of domain experts and novices before
discriminatively distilling their differences to acquire the expert exclusive
knowledge. For the latter, we simulate the evaluation process as book guide to
best accommodate the learning practice of what is accustomed to humans. A
comprehensive human study of 15,000 trials shows our method is able to
consistently improve people of divergent bird expertise to recognise once
unrecognisable birds. Interestingly, our approach also leads to improved
conventional FGVC performance when the extracted knowledge defined is utilised
as means to achieve discriminative localisation. Codes are available at:
https://github.com/PRIS-CV/Making-a-Bird-AI-Expert-Work-for-You-and-Me
