Quantifying and Transferring Contextual Information in Object Detection

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Regularity in the local CR embedding problem

We consider a formally integrable, strictly pseudoconvex CR manifold $M$ of hypersurface type, of dimension $2n-1\geq7$. Local CR, i.e. holomorphic, embeddings of $M$ are known to exist from the works of Kuranishi and Akahori. We address the problem of regularity of the embedding in standard H\"older spaces $C^{a}(M)$, $a\in\mathbf{R}$. If the structure of $M$ is of class $C^{m}$, $m\in\mathbf{Z}$, $4\leq m\leq\infty$, we construct a local CR embedding near each point of $M$. This embedding is of class $C^{a}$, for every $a$, $0\leq a < m+(1/2)$. Our method is based on Henkin's local homotopy formula for the embedded case, some very precise estimates for the solution operators in it, and a substantial modification of a previous Nash-Moser argument due to the second author

Unsupervised learning of generative topic saliency for person re-identification

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data

Open-world Person Re-Identification by Multi-Label Assignment Inference.

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms

Von Neumann entropy and localization-delocalization transition of electron states in quantum small-world networks

The von Neumann entropy for an electron in periodic, disorder and quasiperiodic quantum small-world networks(QSWNs) are studied numerically. For the disorder QSWNs, the derivative of the spectrum averaged von Neumann entropy is maximal at a certain density of shortcut links p*, which can be as a signature of the localization delocalization transition of electron states. The transition point p* is agreement with that obtained by the level statistics method. For the quasiperiodic QSWNs, it is found that there are two regions of the potential parameter. The behaviors of electron states in different regions are similar to that of periodic and disorder QSWNs, respectively.Comment: 6 pages, 13figure