Robust Saliency Detection via Fusing Foreground and Background Priors

Automatic Salient object detection has received tremendous attention from research community and has been an increasingly important tool in many computer vision tasks. This paper proposes a novel bottom-up salient object detection framework which considers both foreground and background cues. First, A series of background and foreground seeds are selected from an image reliably, and then used for calculation of saliency map separately. Next, a combination of foreground and background saliency map is performed. Last, a refinement step based on geodesic distance is utilized to enhance salient regions, thus deriving the final saliency map. Particularly we provide a robust scheme for seeds selection which contributes a lot to accuracy improvement in saliency detection. Extensive experimental evaluations demonstrate the effectiveness of our proposed method against other outstanding methods.Comment: Project website: https://github.com/ChunbiaoZhu/FB

Automatic Salient Object Detection for Panoramic Images Using Region Growing and Fixation Prediction Model

Almost all previous works on saliency detection have been dedicated to conventional images, however, with the outbreak of panoramic images due to the rapid development of VR or AR technology, it is becoming more challenging, meanwhile valuable for extracting salient contents in panoramic images. In this paper, we propose a novel bottom-up salient object detection framework for panoramic images. First, we employ a spatial density estimation method to roughly extract object proposal regions, with the help of region growing algorithm. Meanwhile, an eye fixation model is utilized to predict visually attractive parts in the image from the perspective of the human visual search mechanism. Then, the previous results are combined by the maxima normalization to get the coarse saliency map. Finally, a refinement step based on geodesic distance is utilized for post-processing to derive the final saliency map. To fairly evaluate the performance of the proposed approach, we propose a high-quality dataset of panoramic images (SalPan). Extensive evaluations demonstrate the effectiveness of our proposed method on panoramic images and the superiority of the proposed method against other methods.Comment: Previous Project website: https://github.com/ChunbiaoZhu/DCC-201

Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth) Interventions

We consider the actor-critic contextual bandit for the mobile health (mHealth) intervention. State-of-the-art decision-making algorithms generally ignore the outliers in the dataset. In this paper, we propose a novel robust contextual bandit method for the mHealth. It can achieve the conflicting goal of reducing the influence of outliers while seeking for a similar solution compared with the state-of-the-art contextual bandit methods on the datasets without outliers. Such performance relies on two technologies: (1) the capped-$\ell_{2}$ norm; (2) a reliable method to set the thresholding hyper-parameter, which is inspired by one of the most fundamental techniques in the statistics. Although the model is non-convex and non-differentiable, we propose an effective reweighted algorithm and provide solid theoretical analyses. We prove that the proposed algorithm can find sufficiently decreasing points after each iteration and finally converges after a finite number of iterations. Extensive experiment results on two datasets demonstrate that our method can achieve almost identical results compared with state-of-the-art contextual bandit methods on the dataset without outliers, and significantly outperform those state-of-the-art methods on the badly noised dataset with outliers in a variety of parameter settings

Systematic Study of Diphoton Resonance at 750 GeV from Sgoldstino

The ATLAS and CMS Collaborations of the Large Hadron Collider (LHC) have reported an excess of events in diphoton channel with invariant mass of about 750 GeV. With low energy supersymmetry breaking, we systematically consider the sgoldstino scalar $S$ as the new resonance, which is a linear combination of the CP-even scalar $s$ and CP-odd pseudoscalar $a$. Because we show that $s$ and $a$ can be degenerated or have large mass splitting, we consider two cases for all the following three scenarios: (1) Single resonance. $s$ is the 750 GeV resonance decays to a pair of 1 GeV pseudoscalar $a$. With suitable decay length, these two $a$ decay into collimated pair of photons which cannot be distinguished at the LHC and may appear as diphotons instead of four photons. (2) Twin resonances. $m_{s}\approx m_{a}$ with a mass difference of about 40 GeV and both $s$ and $a$ decay into diphoton pairs. For productions, we consider three scenarios: (I) vector boson fusion; (II) gluon gluon fusion; (III) $q{\bar q}$ pair production. In all these scenarios with two kinds of resonances, we find the parameter space that satisfies the diphoton production cross section from 3 to 13 ${\rm fb}$ and all the other experimental constraints. And we address the decay width as well. In particular, in the third scenario, we observe that the production cross section is small but the decay width of $s$ or $a$ can be from 40 to 60 GeV.Comment: 17 pages, 4 figure

Iterative Normalization: Beyond Standardization towards Efficient Whitening

Batch Normalization (BN) is ubiquitously employed for accelerating neural network training and improving the generalization capability by performing standardization within mini-batches. Decorrelated Batch Normalization (DBN) further boosts the above effectiveness by whitening. However, DBN relies heavily on either a large batch size, or eigen-decomposition that suffers from poor efficiency on GPUs. We propose Iterative Normalization (IterNorm), which employs Newton's iterations for much more efficient whitening, while simultaneously avoiding the eigen-decomposition. Furthermore, we develop a comprehensive study to show IterNorm has better trade-off between optimization and generalization, with theoretical and experimental support. To this end, we exclusively introduce Stochastic Normalization Disturbance (SND), which measures the inherent stochastic uncertainty of samples when applied to normalization operations. With the support of SND, we provide natural explanations to several phenomena from the perspective of optimization, e.g., why group-wise whitening of DBN generally outperforms full-whitening and why the accuracy of BN degenerates with reduced batch sizes. We demonstrate the consistently improved performance of IterNorm with extensive experiments on CIFAR-10 and ImageNet over BN and DBN.Comment: Accepted to CVPR 2019. The Code is available at https://github.com/huangleiBuaa/IterNor

A Multi-length Bunches Design for Electron Storage Rings with Odd Buckets

A scheme with two superconducting RF cavities (sc-cavities) is designed to upgrade electron storage rings with odd buckets into multi-length bunches. In this paper, Hefei Light Source II (HLS II) is given as an example for odd buckets. In accordance with 45 buckets, which is multiples of 3, three different length of bunches generated simultaneously is proposed in the presently applied user optics. The final result is to, without low-alpha optics, fill HLS II with long bunches of 50 ps length, medium bunches of 23 ps and short bunches of 6 ps. Each third buckets can be filled with short bunches, of which the current limit is up to 6.6 mA, more than 60 times the value of low-alpha mode. Moreover, particles tracking about beam dynamics performed by ELEGANT and calculations about beam instabilities are presented in this paper

Lingke: A Fine-grained Multi-turn Chatbot for Customer Service

Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.Comment: Accepted by COLING 2018 demonstration pape

Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning

Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal information between frames. However, cross-modality coherence between audio and video information has not been well addressed during synthesis. In this paper, we propose a novel arbitrary talking face generation framework by discovering the audio-visual coherence via the proposed Asymmetric Mutual Information Estimator (AMIE). In addition, we propose a Dynamic Attention (DA) block by selectively focusing the lip area of the input image during the training stage, to further enhance lip synchronization. Experimental results on benchmark LRW dataset and GRID dataset transcend the state-of-the-art methods on prevalent metrics with robust high-resolution synthesizing on gender and pose variations.Comment: IJCAI-202

Strang-type preconditioners for solving fractional diffusion equations by boundary value methods

The finite difference scheme with the shifted Gr\"{u}nwarld formula is employed to semi-discrete the fractional diffusion equations. This spatial discretization can reduce to the large system of ordinary differential equations (ODEs) with initial values. Recently, boundary value method (BVM) was developed as a popular algorithm for solving large systems of ODEs. This method requires the solutions of one or more nonsymmetric, large and sparse linear systems. In this paper, the GMRES method with the block circulant preconditioner is proposed for solving these linear systems. One of the main results is that if an $A_{\nu_1,\nu_2}$-stable boundary value method is used for an m-by-m system of ODEs, then the preconditioner is invertible and the preconditioned matrix can be decomposed as I+L, where I is the identity matrix and the rank of L is at most $2m(\nu_1+\nu_2)$. It means that when the GMRES method is applied to solve the preconditioned linear systems, the method will converge in at most $2m(\nu_1+\nu_2)+1$ iterations.Finally, extensive numerical experiments are reported to illustrate the effectiveness of our methods for solving the fractional diffusion equations.Comment: 19 pages,4 figure

Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval

Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency. However, the learned hash codes are embedded with limited discriminative semantics due to the intrinsic limitation of image representation. To address the problem, in this paper, we propose a novel hashing approach, dubbed as \emph{Discrete Semantic Transfer Hashing} (DSTH). The key idea is to \emph{directly} augment the semantics of discrete image hash codes by exploring auxiliary contextual modalities. To this end, a unified hashing framework is formulated to simultaneously preserve visual similarities of images and perform semantic transfer from contextual modalities. Further, to guarantee direct semantic transfer and avoid information loss, we explicitly impose the discrete constraint, bit--uncorrelation constraint and bit-balance constraint on hash codes. A novel and effective discrete optimization method based on augmented Lagrangian multiplier is developed to iteratively solve the optimization problem. The whole learning process has linear computation complexity and desirable scalability. Experiments on three benchmark datasets demonstrate the superiority of DSTH compared with several state-of-the-art approaches