Recently, an end-to-end multi-view crowd counting method called multi-view multi-scale (MVMS) has been proposed, which fuses multiple camera views using a CNN to predict a 2D scene-level density map on the ground-plane. Unlike MVMS, we propose to solve the multi-view crowd counting task through 3D feature fusion with 3D scene-level density maps, instead of the 2D ground-plane ones.

In the sense of end-to-end training, the hand-crafted methods used for generating the density maps may not be optimal for the particular network or dataset used. To address this issue, we propose an adaptive density map generator, which takes the annotation dot map as input, and learns a density map representation for training a counter. The counter and generator are trained jointly within an end-to-end framework.

We use a switching hidden Markov model (EMSHMM) approach to analyze eye movement data in cognitive tasks involving cognitive state changes. A high-level state captures a participant’s cognitive state transitions during the task, and eye movement patterns during each high-level state are summarized with a regular HMM.

We propose a ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian Mixtures Models (GMM), assuming that GMMs lie on or near to a manifold that is generated from a low-dimensional hierarchical latent space through parametric mappings. Inspired by Principal Component Analysis (PCA), the generative processes for priors, means and covariance matrices are modeled by
their respective latent space and parametric mapping.

In this project, we focus on the diversity of image captions. First, diversity metrics are proposed which is more correlated to human judgment. Second, we re-evaluate the existing models and find that (1) there is a large gap between human and the existing models in the diversity-accuracy space, (2) using reinforcement learning (CIDEr reward) to train captioning models leads to improving accuracy but reduce diversity. Third, we propose a simple but efficient approach to balance diversity and accuracy via reinforcement learning—using the linear combination of cross-entropy and CIDEr reward.