Occlusion Aware Unsupervised Learning of Optical Flow

It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning. However, the performance of the unsupervised methods still has a relatively large gap compared to its supervised counterpart. Occlusion and large motion are some of the major factors that limit the current unsupervised learning of optical flow methods. In this work we introduce a new method which models occlusion explicitly and a new warping way that facilitates the learning of large motion. Our method shows promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets. Especially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.Comment: CVPR 2018 Camera-read

Effects of system-bath entanglement on the performance of light-harvesting systems: A quantum heat engine perspective

We explore energy transfer in a generic three-level system, which is coupled to three non-equilibrium baths. Built on the concept of quantum heat engine, our three-level model describes non-equilibrium quantum processes including light-harvesting energy transfer, nano-scale heat transfer, photo-induced isomerization, and photovoltaics in double quantum-dots. In the context of light-harvesting, the excitation energy is first pumped up by sunlight, then is transferred via two excited states which are coupled to a phonon bath, and finally decays to the ground state. The efficiency of this process is evaluated by steady state analysis via a polaron-transformed master equation; thus a wide range of the system-phonon coupling strength can be covered. We show that the coupling with the phonon bath not only modifies the steady state, resulting in population inversion, but also introduces a finite steady state coherence which optimizes the energy transfer flux and efficiency. In the strong coupling limit, the steady state coherence disappears and the efficiency approaches the heat engine limit given by Scovil and Schultz-Dubois in Phys. Rew. Lett. 2, 262 (1959).Comment: 10 pages, 8 figures, all comments are welcom

Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy

Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are $l_2$ distance or Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels, such as correntropy, are proposed. However, the current correntropy-based NMF only targets on the low-level features without considering the intrinsic geometrical distribution of data. In this paper, we propose a new NMF algorithm that preserves local invariance by adding graph regularization into the process of max-correntropy-based matrix factorization. Meanwhile, each feature can learn corresponding kernel from the data. The experiment results of Caltech101 and Caltech256 show the benefits of such combination against other NMF algorithms for the unsupervised image clustering