TL;DR
This paper explores how to transfer expert knowledge from fine-grained visual classification AI models to improve human bird recognition skills, using a multi-stage learning framework and human studies.
Contribution
It introduces a novel method to extract expert-exclusive visual knowledge from AI models and demonstrates its effectiveness in enhancing human bird identification abilities.
Findings
Human study with 15,000 trials shows improved bird recognition.
Extracted knowledge enhances traditional FGVC performance.
Method identifies highly discriminative visual regions for training humans.
Abstract
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…
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