The goal of this paper is to review the state-of-the-art progress on visual tracking methods,
classify them into different categories, as well as identify future trends. Visual tracking is a …

Recently, image representation built upon Convolutional Neural Network (CNN) has been
shown to provide effective descriptors for image search, outperforming pre-CNN features as …

Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …

This paper addresses the problem of generating possible object locations for use in object
recognition. We introduce selective search which combines the strength of both an …

Multi-resolution image features may be approximated via extrapolation from nearby scales,
rather than being computed explicitly. This fundamental insight allows us to design object …

Object detection is a key ability required by most computer and robot vision systems. The
latest research on this area has been making great progress in many directions. In the …

Distributional semantic models derive computational representations of word meaning from
the patterns of co-occurrence of words in text. Such models have been a success story of …

For object recognition, the current state-of-the-art is based on exhaustive search. However,
to enable the use of more expensive features and classifiers and thereby progress beyond …

Faster R-CNN achieves state-of-the-art performance on generic object detection. However,
a simple application of this method to a large vehicle dataset performs unimpressively. In …

Object recognition systems constitute a deeply entrenched and omnipresent component of
modern intelligent systems. Research on object recognition algorithms has led to advances …