Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR
occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal …

In this paper we address three different computer vision tasks using a single basic
architecture: depth prediction, surface normal estimation, and semantic labeling. We use a …

Low-level saliency cues or priors do not produce good enough saliency detection results
especially when the salient object presents in a low-contrast background with confusing …

In this paper, we focus on complex event detection in internet videos while also providing
the key evidences of the detection results. Convolutional Neural Networks (CNNs) have …

Extracting informative image features and learning effective approximate hashing functions
are two crucial steps in image retrieval. Conventional methods often study these two steps …

In the past few years, convolutional neural nets (CNN) have shown incredible promise for
learning visual representations. In this paper, we use CNNs for the task of predicting surface …

Traditional architectures for solving computer vision problems and the degree of success
they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep …

Nonlinear regression has been extensively employed in many computer vision problems
(eg, crowd counting, age estimation, affective computing). Under the umbrella of deep …

Abstract Convolutional Neural Networks (ConvNets) have successfully contributed to
improve the accuracy of regression-based methods for computer vision tasks such as …