Recognition is graduating from labs to real-world applications. While it is encouraging to see
its potential being tapped, it brings forth a fundamental challenge to the vision researcher …

How can we know whether a statement about our world is valid. For example, given a
relationship between a pair of entities eg,eat (horse, hay)', how can we know whether this …

This paper studies efficient means in dealing with intracategory diversity in object detection.
Strategies for occlusion and orientation handling are explored by learning an ensemble of …

Vehicle detection is a challenging problem in autonomous driving systems, due to its large
structural and appearance variations. In this paper, we propose a novel vehicle detection …

The marriage between the deep convolutional neural network (CNN) and region proposals
has made breakthroughs for object detection in recent years. While the discriminative object …

Deep neural networks (DNNs) have become the gold standard for solving challenging
classification problems, especially given complex sensor inputs (eg, images and video) …

Deep learning technology has promoted the object detection task in the remote sensing
(RS) field to move toward better performance and more demanding requirements. Except for …

Learning models for object detection is a challenging problem due to the large intra-class
variability of objects in appearance, viewpoints, and rigidity. We address this variability by a …

Abstract Recent Large-Scale Multimedia Retrieval (LSMR) methods seem to heavily rely on
analysing a large amount of data using high-performance machines. This paper aims to …

Object classes generally contain large intra-class variation, which poses a challenge to
object detection schemes. In this work, we study visual subcategorization as a means of …