80,705 research outputs found
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- 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 as the new resonance, which is a linear combination of
the CP-even scalar and CP-odd pseudoscalar . Because we show that
and can be degenerated or have large mass splitting, we consider two cases
for all the following three scenarios: (1) Single resonance. is the 750 GeV
resonance decays to a pair of 1 GeV pseudoscalar . With suitable decay
length, these two 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. with a mass difference of about 40
GeV and both and decay into diphoton pairs. For productions, we
consider three scenarios: (I) vector boson fusion; (II) gluon gluon fusion;
(III) 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 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 or 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 -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 . It means that when the GMRES
method is applied to solve the preconditioned linear systems, the method will
converge in at most 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
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