Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

The performance on deep learning is significantly affected by volume of training data.
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for …

The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …

In this paper, we tackle the problem of domain generalization: how to learn a generalized
feature representation for an “unseen” target domain by taking the advantage of multiple …

In this paper, we propose a new unsupervised domain adaptation approach called
Collaborative and Adversarial Network (CAN) through domain-collaborative and domain …

Visual object tracking is an important area in computer vision, and many tracking algorithms
have been proposed with promising results. Existing object tracking approaches can be …

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

The recent success of deep neural networks relies on massive amounts of labeled data. For
a target task where labeled data is unavailable, domain adaptation can transfer a learner …

Unlike human learning, machine learning often fails to handle changes between training
(source) and test (target) input distributions. Such domain shifts, common in practical …