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 …

The complexity of multimedia contents is significantly increasing in the current digital world.
This yields an exigent demand for develo** highly effective retrieval systems to satisfy …

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for
object detection. While almost all state-of-the-art object detectors utilize predefined anchors …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

While most approaches to semantic reasoning have focused on improving performance, in
this paper we argue that computational times are very important in order to enable real time …

Applying convolutional neural networks to large images is computationally expensive
because the amount of computation scales linearly with the number of image pixels. We …

Effective multi-object tracking (MOT) methods have been developed in recent years for a
wide range of applications including visual surveillance and behavior understanding …

The core component of most modern trackers is a discriminative classifier, tasked with
distinguishing between the target and the surrounding environment. To cope with natural …

This paper introduces VEDAI: Vehicle Detection in Aerial Imagery a new database of aerial
images provided as a tool to benchmark automatic target recognition algorithms in …