CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 201

Noise-induced dynamics and photon statistics in bimodal quantum-dot micropillar lasers

Emission characteristics of quantum-dot micropillar lasers (QDMLs) are located at the intersection of nanophotonics and nonlinear dynamics, which provides an ideal platform for studying the optical interface between classical and quantum systems. In this work, a noise-induced bimodal QDML with orthogonal dual-mode outputs is modeled, and nonlinear dynamics, stochastic mode jumping and quantum statistics with the variation of stochastic noise intensity are investigated. Noise-induced effects lead to the emergence of two intensity bifurcation points for the strong and the weak mode, and the maximum output power of the strong mode becomes larger as the noise intensity increases. The anti-correlation of the two modes reaches the maximum at the second intensity bifurcation point. The dual-mode stochastic jumping frequency and effective bandwidth can exceed 100 GHz and 30 GHz under the noise-induced effect. Moreover, the noise-induced photon correlations of both modes simultaneously exhibit super-thermal bunching effects ($g^{(2)}(0)>2$) in the low injection current region. The $g^{(2)}(0)$-value of the strong mode can reach over 6 in the high injection current region. Photon bunching ($g^{(2)}(0)>1$) of both modes is observed over a wide range of noise intensities and injection currents. In the presence of the noise-induced effect, the photon number distribution of the strong or the weak mode is a mixture of Bose-Einstein and Poisson distributions. As the noise intensity increases, the photon number distribution of the strong mode is dominated by the Bose-Einstein distribution, and the proportion of the Poisson distribution is increased in the high injection current region, while that of the weak mode is reduced. Our results contribute to the development preparation of super-bunching quantum integrated light sources for improving the spatiotemporal resolution of quantum sensing measurements.Comment: 17 pages, 9 figure

The effects of rapid urbanization on forest landscape connectivity in Zhuhai city, China

Urban forests can provide the necessary ecosystem services for their residents and play an important part in improving the urban environment. Forest landscape connectivity is a vital indicator reflecting the quality of the ecological environment and ecological functions. Detecting changes in landscape connectivity is, therefore, an important step for providing sound scientific evidence for the better urban planning. Using remote sensing images of a study area in Zhuhai City in 1999, 2005, 2009 and 2013, the dynamic forest landscape connectivity of Zhuhai city can be evaluated based on a graph-theoretic approach. The aims of our study were to discover and interpret the effect of rapid urbanization on forest landscape connectivity. The construction of ecological corridors helps us specifically compare the landscape connectivity of three parts of urban forests. On the basis of functional landscape metrics, the correlation of these metrics and patch area was discussed in order to comprehensively identify the key patches. The analysis showed that the total areas of forestlands reduced from 1999 to 2009 and then increased from 2009 to 2013, and the same trend was found in overall forest landscape connectivity. To improve the overall landscape connectivity, construct urban ecological network and appropriately protect biodiversity in the future, the existing important patches with large areas or key positions should be well protected. This study revealed that urbanization reduced the area of key patches and consequently reduced the forest landscape connectivity, which increased while the patch areas increased due to the environmental protection policy. Functional connectivity indicators could provide more comprehensive information in the development of environmental protection strategies

Prognostics for an actuator with the combination of support vector regression and particle filter

The accurate prognostics for actuator malfunctions is a challenging task. Developing reliable prognostic methods is vital for providing reasonable preventive maintenance schedules and preventing unexpected failures. Particle filter has been proved to be a traditional approach to deal with actuator prognostic problems. However, the measurement function in the particle filter algorithm cannot be obtained in the prediction process, this paper presents a hybrid framework combining support vector regression (SVR) and particle filter (PF). The SVR output prediction results are employed as the “measurements” for the subsequent PF algorithm. To accomplish the accurate prognostics for actuator fault of civil aircraft, an improved PF based on Kendall correlation coefficient is put forward to solve the problem of particles’ degeneracy. The experimental results are presented, demonstrating that the SVR-PF hybrid approach has satisfactory performance with better prognostics accuracy and higher fault resolution than traditional approaches

LBS: Loss-aware Bit Sharing for Automatic Model Compression

Low-bitwidth model compression is an effective method to reduce the model size and computational overhead. Existing compression methods rely on some compression configurations (such as pruning rates, and/or bitwidths), which are often determined manually and not optimal. Some attempts have been made to search them automatically, but the optimization process is often very expensive. To alleviate this, we devise a simple yet effective method named Loss-aware Bit Sharing (LBS) to automatically search for optimal model compression configurations. To this end, we propose a novel single-path model to encode all candidate compression configurations, where a high bitwidth quantized value can be decomposed into the sum of the lowest bitwidth quantized value and a series of re-assignment offsets. We then introduce learnable binary gates to encode the choice of bitwidth, including filter-wise 0-bit for filter pruning. By jointly training the binary gates in conjunction with network parameters, the compression configurations of each layer can be automatically determined. Extensive experiments on both CIFAR-100 and ImageNet show that LBS is able to significantly reduce computational cost while preserving promising performance.Comment: 22 page