  Current pre-training methods in computer vision focus on natural images in
the daily-life context. However, abstract diagrams such as icons and symbols
are common and important in the real world. This work is inspired by Tangram, a
game that requires replicating an abstract pattern from seven dissected shapes.
By recording human experience in solving tangram puzzles, we present the
Tangram dataset and show that a pre-trained neural model on the Tangram helps
solve some mini visual tasks based on low-resolution vision. Extensive
experiments demonstrate that our proposed method generates intelligent
solutions for aesthetic tasks such as folding clothes and evaluating room
layouts. The pre-trained feature extractor can facilitate the convergence of
few-shot learning tasks on human handwriting and improve the accuracy in
identifying icons by their contours. The Tangram dataset is available at
https://github.com/yizhouzhao/Tangram.
