Discussion of "Estimating Random Effects via Adjustment for Density Maximization" by C. Morris and R. Tang

Discussion of "Estimating Random Effects via Adjustment for Density Maximization" by C. Morris and R. Tang [arXiv:1108.3234]Comment: Published in at http://dx.doi.org/10.1214/11-STS349A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Rejoinder

Rejoinder of "Estimating Random Effects via Adjustment for Density Maximization" by C. Morris and R. Tang [arXiv:1108.3234]Comment: Published in at http://dx.doi.org/10.1214/11-STS349REJ the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Deep Primal-Dual Network for Guided Depth Super-Resolution

In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.Comment: BMVC 201

Extension of a theorem of Duffin and Schaeffer

Let $r_1,\ldots,r_s:\mathbb{Z}_{n\geqslant 0}\to\mathbb{C}$ be linearly recurrent sequences whose associated eigenvalues have arguments in $\pi\mathbb{Q}$ and let $F(z):=\sum_{n\geqslant 0}f(n)z^n$, where $f(n)\in\{r_1(n),\ldots,$ $r_s(n)\}$ for each $n\geqslant 0$. We prove that if $F(z)$ is bounded in a sector of its disk of convergence, it is a rational function. This extends a very recent result of Tang and Wang, who gave the analogous result when the sequence $f(n)$ takes on values of finitely many polynomials.Comment: 2 page

Deep GrabCut for Object Selection

Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest. However it is common that a rectangle input could be too large or too small. In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map. A convolutional encoder-decoder network is trained end-to-end by concatenating images with these distance maps as inputs and predicting the object masks as outputs. Our approach gets accurate segmentation results given sloppy rectangles while being general for both interactive segmentation and instance segmentation. We show our network extends to curve-based input without retraining. We further apply our network to instance-level semantic segmentation and resolve any overlap using a conditional random field. Experiments on benchmark datasets demonstrate the effectiveness of the proposed approaches.Comment: BMVC 201

Recognizing and Curating Photo Albums via Event-Specific Image Importance

Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection. In this paper, we attempt to simultaneously solve both tasks: album-wise event recognition and image- wise importance prediction. We collected an album dataset with both event type labels and image importance labels, refined from an existing CUFED dataset. We propose a hybrid system consisting of three parts: A siamese network-based event-specific image importance prediction, a Convolutional Neural Network (CNN) that recognizes the event type, and a Long Short-Term Memory (LSTM)-based sequence level event recognizer. We propose an iterative updating procedure for event type and image importance score prediction. We experimentally verified that image importance score prediction and event type recognition can each help the performance of the other.Comment: Accepted as oral in BMVC 201

Multispectral Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora

Consistency in Aggregation of GDP Indexes and Uniqueness of Quantity and Price Effects on Growth of GDP and Aggregate Labor Productivity

In traditional decomposition of GDP growth in constant prices, an industry’s contribution consisted only of a quantity effect from GDP growth. Tang and Wang’s (2004, 2014) innovation added a price effect from relative price change. Dumagan (2013a, 2016) showed that Tang and Wang’s quantity and price effects for all industries exactly add up to growth of GDP either in chained or in constant prices, that is, regardless of the GDP index. However, this paper shows that it is only when GDP is in chained prices and the GDP index is consistent-in-aggregation (CIA) that quantity and price effects are invariant with industry regroupings, that is, unique. Therefore, Tang and Wang’s (2004, 2014) growth decompositions in Canada and US—where GDP is in chained prices based on the Fisher index—yield effects that vary with industry regroupings because the Fisher index is not CIA. This variation prevents attributing unique price and quantity effects to industries and, thus, clouds Tang and Wang’s analysis of the role of industries in GDP growth and in aggregate labor productivity growth. This paper also examines price and quantity effects on GDP growth of representative countries with GDP different from that in the US to make the results globally relevant