Coconditional Autoencoding Adversarial Networks for Chinese Font Feature Learning

In this work, we propose a novel framework named Coconditional Autoencoding Adversarial Networks (CocoAAN) for Chinese font learning, which jointly learns a generation network and two encoding networks of different feature domains using an adversarial process. The encoding networks map the glyph images into style and content features respectively via the pairwise substitution optimization strategy, and the generation network maps these two kinds of features to glyph samples. Together with a discriminative network conditioned on the extracted features, our framework succeeds in producing realistic-looking Chinese glyph images flexibly. Unlike previous models relying on the complex segmentation of Chinese components or strokes, our model can "parse" structures in an unsupervised way, through which the content feature representation of each character is captured. Experiments demonstrate our framework has a powerful generalization capacity to other unseen fonts and characters.

Qiniu Submission to ActivityNet Challenge 2018

In this paper, we introduce our submissions for the tasks of trimmed activity recognition (Kinetics) and trimmed event recognition (Moments in Time) for Activitynet Challenge 2018. In the two tasks, non-local neural networks and temporal segment networks are implemented as our base models. Multi-modal cues such as RGB image, optical flow and acoustic signal have also been used in our method. We also propose new non-local-based models for further improvement on the recognition accuracy. The final submissions after ensembling the models achieve 83.5% top-1 accuracy and 96.8% top-5 accuracy on the Kinetics validation set, 35.81% top-1 accuracy and 62.59% top-5 accuracy on the MIT validation set.