This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision system…

The Jaccard loss, commonly referred to as the intersection-over-union loss, is commonly employed in the evaluation of segmentation quality due to its…

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameter…

Learning the distribution of images in order to generate new samples is a challenging task due to the high dimensionality of the data and the highly …

We study the computational complexity certification of inexact gradient augmented Lagrangian methods for solving convex optimization problems with co…

We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF). "Best-scored" means to…

Low rank tensor learning, such as tensor completion and multilinear multitask learning, has received much attention in recent years. In this paper, w…

The goal of domain adaptation is to adapt models learned on a source domain to a particular target domain. Most methods for unsupervised domain adapt…

Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems. However, reg…

Convolutional neural networks rely on image texture and structure to serve as discriminative features to classify the image content. Image enhancemen…

Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular bo…

The one-bit quantization is implemented by one single comparator that operates at low power and a high rate. Hence one-bit compressive sensing (1bit-…

In this paper we evaluate the quality of the activation layers of a convolutional neural network (CNN) for the gen- eration of object proposals. We g…

In this paper, we proposed a novel pipeline for image-level classification in the hyperspectral images. By doing this, we show that the discriminativ…

Quantifying similarity between data objects is an important part of modern data science. Deciding what similarity measure to use is very application …

Image enhancement using the visible (V) and near-infrared (NIR) usually enhances useful image details. The enhanced images are evaluated by observers…

Lane detection is typically tackled with a two-step pipeline in which a segmentation mask of the lane markings is predicted first, and a lane line mo…

Image-to-image (I2I) translation is a pixel-level mapping that requires a large number of paired training data and often suffers from the problems of…

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant durin…

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. …

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