4,513 research outputs found
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
For human pose estimation in monocular images, joint occlusions and
overlapping upon human bodies often result in deviated pose predictions. Under
these circumstances, biologically implausible pose predictions may be produced.
In contrast, human vision is able to predict poses by exploiting geometric
constraints of joint inter-connectivity. To address the problem by
incorporating priors about the structure of human bodies, we propose a novel
structure-aware convolutional network to implicitly take such priors into
account during training of the deep network. Explicit learning of such
constraints is typically challenging. Instead, we design discriminators to
distinguish the real poses from the fake ones (such as biologically implausible
ones). If the pose generator (G) generates results that the discriminator fails
to distinguish from real ones, the network successfully learns the priors.Comment: Fixed typos. 14 pages. Demonstration videos are
http://v.qq.com/x/page/c039862eira.html,
http://v.qq.com/x/page/f0398zcvkl5.html,
http://v.qq.com/x/page/w0398ei9m1r.htm
A unified approach to exact solutions of time-dependent Lie-algebraic quantum systems
By using the Lewis-Riesenfeld theory and the invariant-related unitary
transformation formulation, the exact solutions of the {\it time-dependent}
Schr\"{o}dinger equations which govern the various Lie-algebraic quantum
systems in atomic physics, quantum optics, nuclear physics and laser physics
are obtained. It is shown that the {\it explicit} solutions may also be
obtained by working in a sub-Hilbert-space corresponding to a particular
eigenvalue of the conserved generator ({\it i. e.}, the {\it time-independent}
invariant) for some quantum systems without quasi-algebraic structures. The
global and topological properties of geometric phases and their adiabatic limit
in time-dependent quantum systems/models are briefly discussed.Comment: 11 pages, Latex. accepted by Euro. Phys. J.
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Face Super-Resolution (SR) is a domain-specific super-resolution problem. The
specific facial prior knowledge could be leveraged for better super-resolving
face images. We present a novel deep end-to-end trainable Face Super-Resolution
Network (FSRNet), which makes full use of the geometry prior, i.e., facial
landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR)
face images without well-aligned requirement. Specifically, we first construct
a coarse SR network to recover a coarse high-resolution (HR) image. Then, the
coarse HR image is sent to two branches: a fine SR encoder and a prior
information estimation network, which extracts the image features, and
estimates landmark heatmaps/parsing maps respectively. Both image features and
prior information are sent to a fine SR decoder to recover the HR image. To
further generate realistic faces, we propose the Face Super-Resolution
Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss
into FSRNet. Moreover, we introduce two related tasks, face alignment and
parsing, as the new evaluation metrics for face SR, which address the
inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark
experiments show that FSRNet and FSRGAN significantly outperforms state of the
arts for very LR face SR, both quantitatively and qualitatively. Code will be
made available upon publication.Comment: Chen and Tai contributed equally to this pape
Hidden Tree Structure is a Key to the Emergence of Scaling in the World Wide Web
Preferential attachment is the most popular explanation for the emergence of
scaling behavior in the World Wide Web, but this explanation has been
challenged by the global information hypothesis, the existence of linear
preference and the emergence of new big internet companies in the real world.
We notice that most websites have an obvious feature that their pages are
organized as a tree (namely hidden tree) and hence propose a new model that
introduces a hidden tree structure into the Erd\H{o}s-R\'e}yi model by adding a
new rule: when one node connects to another, it should also connect to all
nodes in the path between these two nodes in the hidden tree. The experimental
results show that the degree distribution of the generated graphs would obey
power law distributions and have variable high clustering coefficients and
variable small average lengths of shortest paths. The proposed model provides
an alternative explanation to the emergence of scaling in the World Wide Web
without the above-mentioned difficulties, and also explains the "preferential
attachment" phenomenon.Comment: 4 Pages, 7 Figure
Multiplicative Noise Removal with a Sparsity-Aware Optimization Model
Restoration of images contaminated by multiplicative noise (also known as speckle noise) is a key issue in coherent image processing. Notice that images under consideration are often highly compressible in certain suitably chosen transform domains. By exploring this intrinsic feature embedded in images, this paper introduces a variational restoration model for multiplicative noise reduction that consists of a term reflecting the observed image and multiplicative noise, a quadratic term measuring the closeness of the underlying image in a transform domain to a sparse vector, and a sparse regularizer for removing multiplicative noise. Being different from popular existing models which focus on pursuing convexity, the proposed sparsity-aware model may be nonconvex depending on the conditions of the parameters of the model for achieving the optimal denoising performance. An algorithm for finding a critical point of the objective function of the model is developed based on coupled fixed-point equations expressed in terms of the proximity operator of functions that appear in the objective function. Convergence analysis of the algorithm is provided. Experimental results are shown to demonstrate that the proposed iterative algorithm is sensitive to some initializations for obtaining the best restoration results. We observe that the proposed method with SAR-BM3D filtering images as initial estimates can remarkably outperform several state of-art methods in terms of the quality of the restored images
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