A Saliency Dataset of Head and Eye Movements for Augmented Reality

In augmented reality (AR), correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources for the analysing, rendering and 3D registration on the areas of interest. However, there is no research about understanding the visual exploration of users when using an AR system or modeling AR visual attention. To bridge the gap between the real-world scene and the scene augmented by virtual information, we construct the ARVR saliency dataset with 100 diverse videos evaluated by 20 people. The virtual reality (VR) technique is employed to simulate the real-world, and annotations of object recognition and tracking as augmented contents are blended into the omnidirectional videos. Users can get the sense of experiencing AR when watching the augmented videos. The saliency annotations of head and eye movements for both original and augmented videos are collected which constitute the ARVR dataset.

Deep Unsupervised Clustering with Clustered Generator Model

This paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clustered generator model for clustering which contains both continuous and discrete latent variables. Discrete latent variables model the cluster label while the continuous ones model variations within each cluster. The learning of the model proceeds in a unified probabilistic framework and incorporates the unsupervised clustering as an inner step without the need for an extra inference model as in existing variational-based models. The latent variables learned serve as both observed data embedding or latent representation for data distribution. Our experiments show that the proposed model can achieve competitive unsupervised clustering accuracy and can learn disentangled latent representations to generate realistic samples. In addition, the model can be naturally extended to per-pixel unsupervised clustering which remains largely unexplored.