As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

Much of vision-and-language research focuses on a small but diverse set of independent
tasks and supporting datasets often studied in isolation; however, the visually-grounded …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

There is a growing interest in learning data representations that work well for many different
types of problems and data. In this paper, we look in particular at the task of learning a single …

With efficient appearance learning models, discriminative correlation filter (DCF) has been
proven to be very successful in recent video object tracking benchmarks and competitions …

Multi-task learning in Convolutional Networks has displayed remarkable success in the field
of recognition. This success can be largely attributed to learning shared representations …

We propose a novel visual tracking algorithm based on the representations from a
discriminatively trained Convolutional Neural Network (CNN). Our algorithm pretrains a …

Tracking by siamese networks has achieved favorable performance in recent years.
However, most of existing siamese methods do not take full advantage of spatial-temporal …

The boom in the capabilities and features of mobile devices, like smartphones, tablets, and
wearables, combined with the ubiquitous and affordable Internet access and the advances …