Improving Filling Level Classification with Adversarial Training
Improving Filling Level Classification with Adversarial Training
We investigate the problem of classifying–from a single image–the level of content in a cup or a drinking glass. This problem is made challenging by several ambiguities caused by transparencies, shape variations and partial occlusions, and by the availability of only small training datasets. In this paper, we tackle this …