Our results imply that the Margin based informativeness measure consistently outperforms other measures. We also show that active learning brings definitive advantages in challenging databases when accompanied with powerful feature representations.
Our evaluations demonstrate that both taxonomy and distributional similarity serve as useful sources of information for attribute nomination, and our methods can effectively exploit them.
Deep Stroke-based Sketched Symbol Reconstruction and Segmentation
We propose a neural network model that segments symbols into stroke-level components. Our segmentation framework has two main elements: a fixed feature extractor and a Multilayer Perceptron (MLP) network that identifies a component based on the feature
DPFrag is an efficient, globally optimal fragmentation method that learns segmentation parameters from data and produces fragmentations by combining primitive recognizers in a dynamic-programming framework. The fragmentation is fast and doesn’t require laborious and tedious parameter tuning. In experiments, it beat state-of-the-art methods on standard databases with only a handful of labeled examples.
Sketch Recognition with Few Examples
Our systems perform self-learning by automatically extending a very small set of labeled examples with new examples extracted from unlabeled sketches.