Due to object detection's close relationship with video analysis and image understanding, it
has attracted much research attention in recent years. Traditional object detection methods …

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

We propose a semi-supervised approach for contemporary object detectors following the
teacher-student dual model framework. Our method is featured with 1) the exponential …

Automatic detection of pavement cracks is an important task in transportation maintenance
for driving safety assurance. However, it remains a challenging task due to the intensity …

A computer-aided diagnosis (CAD) system requires detection, segmentation, and
classification in one framework to assist radiologists efficiently in an accurate diagnosis. In …

We present a memory and computation efficient ternary weight networks (TWNs)-with
weights constrained to+ 1, 0 and-1. The Euclidian distance between full (float or double) …

The performance of object detection has recently been significantly improved due to the
powerful features learnt through convolutional neural networks (CNNs). Despite the …

Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

Almost all of the current top-performing object detection networks employ region proposals
to guide the search for object instances. State-of-the-art region proposal methods usually …

We present RON, an efficient and effective framework for generic object detection. Our
motivation is to smartly associate the best of the region-based (eg, Faster R-CNN) and …