Gradient-boosted decision trees (GBDTs) are widely used in machine learning, and the output of current GBDT implementations is a single variable. When there are multiple outputs, GBDT constructs multiple trees corresponding to the output variables. The correlations between variables are ignored by such a strategy causing redundancy of the learned tree structures. In this article, we propose a general method to learn GBDT for multiple outputs, called GBDT-MO. Each leaf of GBDT-MO constructs predictions of all variables or a subset of automatically selected variables. This is achieved by considering the summation of objective gains over all output variables. Moreover, we extend histogram approximation into the multiple-output case to speed up training. Various experiments on synthetic and real data sets verify that GBDT-MO achieves outstanding performance in terms of accuracy, training speed, and inference speed.

Dilated convolutions support expanding receptive field without parameter exploration or resolution loss, which turn out to be suitable for pixel-level prediction problems. In this paper, we propose multiscale single image super-resolution (SR) based on dilated convolutions. We adopt dilated convolutions to expand the receptive field size without incurring additional computational complexity. We mix standard convolutions and dilated convolutions in each layer, called mixed convolutions, i.e., in the mixed convolutional layer, and the feature extracted by dilated convolutions and standard convolutions are concatenated. We theoretically analyze the receptive field and intensity of mixed convolutions to discover their role in SR. Mixed convolutions remove blind spots and capture the correlation between low-resolution (LR) and high-resolution (HR) image pairs successfully, thus achieving good generalization ability. We verify those properties of mixed convolutions by training 5-layer and 10-layer networks. We also train a 20-layer deep network to compare the performance of the proposed method with those of the state-of-the-art ones. Moreover, we jointly learn maps with different scales from a LR image to its HR one in a single network. Experimental results demonstrate that the proposed method outperforms the state-of-the-art ones in terms of PSNR and SSIM, especially for a large-scale factor.

In this paper, we propose deep feature embedding learning for person re-identification (re-id) using lifted structured loss. Although triplet loss has been commonly used in deep neural networks for person re-id, the triplet loss-based framework is not effective in fully using the batch information. Thus, it needs to choose hard negative samples manually that is very time-consuming. To address this problem, we adopt lifted structured loss for deep neural networks that makes the network learn better feature embedding by minimizing intra-class variation and maximizing inter-class variation. Extensive experiments on CUHK03, CUHKOI and VIPeR datasets demonstrate the superior performance of the proposed method over state-of-the-arts in terms of the cumulative match curve (CMC) metric.

It is required that input features are represented as vectors or scalars in machine learning for classification, e.g. support vector machine (SVM). However, real world data such as 2D images is naturally represented as matrices or tensors with higher dimensions. Thus, structural information of the data whose dimensions are more than two is not successfully considered. One typical structural information which is useful for the classification task is the spatial relationship of the nearby data points. In this paper, to leverage this kind of structural information, we propose a novel classification method which combines total variational (TV) regularization with SVM, called TV-SVM. Since TV achieves a local smoothing property by penalizing the local discontinuity of data, TV-SVM preserves better local structure than the original SVM due to TV regularization. We solve the objective function of TV-SVM via the alternating direction method of multipliers (ADMM) algorithm. Experimental results on image classification show that TV-SVM is competitive to the state-of-the-art learning method in both classification accuracy and computational complexity.

In this paper, we propose variational fusion of time-of-flight (TOF) and stereo data for depth estimation using edge-selective joint filtering (ESJF). ESJF is able to adaptively select edges for depth upsampling from the TOF depth map, stereo matching-based disparity map, and stereo images. We adopt ESJF to produce high-resolution (HR) depth maps with accurate edge information from low-resolution ones captured by the TOF camera. First, we measure confidences of TOF and stereo data based on a Gaussian function to be used as fusion weights. Then, we upsample the TOF depth map using ESJF and extract vertical and horizontal discontinuity maps from it. Finally, we perform variational fusion of TOF and stereo depth data guided by the discontinuity maps. Experimental results show that the proposed method successfully produces HR depth maps and outperforms the state of the art in preserving edges and removing noise.

In this paper, we propose variational fusion of time-of-flight (TOF) and stereo data using edge selective joint filtering (ESJF). We utilize ESJF to up-sample low-resolution (LR) depth captured by TOF camera and produce high-resolution (HR) depth maps with accurate edge information. First, we measure confidence of two sensor with different reliability to fuse them. Then, we up-sample TOF depth map using ESJF to generate discontinuity maps and protect edges in depth. Finally, we perform variational fusion of TOF and stereo depth data based on total variation (TV) guided by discontinuity maps. Experimental results show that the proposed method successfully produces HR depth maps and outperforms the-state-of-the-art ones in preserving edges and removing noise.

The weighted least squares (WLS) filter is a well-known edge preserving smoothing technique, but its weights highly depend on the image gradients. Thus, it is hard to smooth local textures with large gradients by WLS filter. In this paper, we propose a structure tensor-based WLS (ST-WLS) filter to enhance the smoothing ability of WLS filter while successfully keeping object boundaries. We introduce structure tensor into WLS filter to extract local structures in an image and use them to guide the smoothing process. Specifically, we use structure tensor to measure isotropy of pixels. Then, we calculate the smoothing weights for pixels based on both their isotropy and gradients. Experimental results demonstrate that ST-WLS filter has strong ability of smoothing textures, especially for isotropic textures, while preserving boundaries and structures of objects.