Games are popular media among youth since they are not only entertaining but also provide exotic experiences and establish relationships between players. In this work, we propose a game-based learning tool for Chemistry called Elemem which aims to 1) demonstrate the relevance of the periodic table in the context of everyday life in order to improve the understanding of the periodic table and its elements, and 2) provide an alternative and attractive learning tool for use both at school (group-learning) and at home (self-learning). Several experiments reveal that Elemem is not only an enjoyable and encouraging self-learning process, but also educational; 79% of the students said they will recommend Elemem to their friends and others.

In this paper, we explore deep learning methods for estimating when the objects were made. Temporal estimation of objects is a challenging task which requires expertise in the object domain. With temporal information of objects, historian, genealogists, sociologist, archaeologist or conservationists can study the past through the objects. Toward this goal, we utilize features from existing deep networks and fine-tune new networks for temporal estimation task. The results demonstrate that the deep learning approach outperforms both a color-based baseline and visual data mining approach which is the previous state of the art method for the temporal estimation. To gain the insights into the deep network performance, we provide the analyses of neuron activations and their entropy including neuron temporal sensitivity, neuron activity and the correlation between discriminative parts from the deep network and the data mining approach. Finally, we demonstrate the potential of the temporal estimation pipeline for an interesting application such as fashion trend analysis.

Clothing and fashion are an integral part of our everyday lives. In this paper we present an approach to studying fashion both on the runway and in more real-world settings, computationally, and at large scale, using computer vision. Our contributions include collecting a new runway dataset, designing features suitable for capturing outfit appearance, collecting human judgments of outfit similarity, and learning similarity functions on the features to mimic those judgments. We provide both intrinsic and extrinsic evaluations of our learned models to assess performance on outfit similarity prediction as well as season, year, and brand estimation. An example application tracks visual trends as runway fashions filter down to "real way" street fashions.

As computer vision datasets grow larger, the community is increasingly relying on crowdsourced annotations to train and test their algorithms. Since the capability of online annotators is considered varied and unpredictable, many strategies have been proposed to “clean ” crowdsourced annotations. However, these strategies typically require more annotations, rather than using the annotation or image content itself. In this paper we propose and evaluate several strategies for automatically estimating the quality of an object annotation. Finally, we show that we can significantly outperform simple baselines by combining multiple image-based annotation assessment strategies. 1