In light of the recent success of artificial intelligence (AI) in computer vision applications,
many researchers and physicians expect that AI would be able to assist in many tasks in …

Pedestrian detection is a key problem in computer vision, with several applications that have
the potential to positively impact quality of life. In recent years, the number of approaches to …

Abstract We propose'Hide-and-Seek', a weakly-supervised framework that aims to improve
object localization in images and action localization in videos. Most existing weakly …

We propose a novel unsupervised learning approach to build features suitable for object
detection and classification. The features are pre-trained on a large dataset without human …

The research focus of designing local patch descriptors has gradually shifted from
handcrafted ones (eg, SIFT) to learned ones. In this paper, we propose to learn high per …

In recent years, deep learning-based algorithms have brought great improvements to rigid
object detection. In addition to rigid objects, remote sensing images also contain many …

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized
single hidden layer feedforward neural networks, of which the hidden node parameters are …

Cross-view geo-localization is to spot images of the same geographic target from different
platforms, eg, drone-view cameras and satellites. It is challenging in the large visual …

Successful visual object recognition methods typically rely on training datasets containing
lots of richly annotated images. Annotating object bounding boxes is both expensive and …

We study the problem of object recognition for categories for which we have no training
examples, a task also called zero--data or zero-shot learning. This situation has hardly been …