Tutorial 2 – Deep learning for visual computing and image processing
December 1st, 2019.
Tutorials are included with your registration. Buy this product only if you wish to attend a tutorial and do not the full VCIP 2019 conference.
During the past few years, deep learning techniques have achieved great success in various computer vision tasks, such as recognition, detection, tracking, and so on. Compared to traditional AI models, deep learning techniques are generally much more powerful for their impressive representation capability. Some of the state-of-the-art deep learning models can even surpass human-level performance in visual computation and image processing. Deep learning is now shaping the future of AI, as well as the human society.
In this tutorial, we will introduce the fundamental deep learning techniques for visual computation and image processing and review the recent progress in different sub-areas of deep learning. First, we will discuss the general development of deep learning and AI. Some representative advancements will be introduced to help illustrate the implementation from perceiving to learning, reasoning and behaving, delivering a broad view of current deep learning techniques and challenges that lie ahead. Second, we will discuss the domain shift problem in deep learning and introduce deep domain adaptation approaches that can tackle this problem effectively. Next, we will focus on the structure in visual data that can provide rich information to help improve the performance of deep models. Recent progress in using deep learning to model the structure in visual data will be reviewed and discussed in details. Following this, we will discuss the concept of generative adversarial networks (GANs) in deep learning. Different adversarial losses and adversarial strategies that can stabilize the training of (GANs) will be analysed and discussed. Then we will introduce the recent progress of deep learning based person re-identification methods. In particular, the analysis on studies about supervised person re-id and unsupervised person re-id will be presented with details. Lastly, we will introduce how generic object detection is implemented using deep learning techniques. We will describe the most representative deep learning-based object detectors proposed in the last few years. To sum up, in this tutorial, we will introduce the latest technologies on deep learning for visual computation and image processing with rich in-depth analysis and insightful discussions.
See http://www.multimediauts.org/VCIP_2019_V1/dl_tutorials.html?id=f24near for full details
Cancellation and Refunds
- Cancellations before 30 Oct. 2019 will be subject to charge 20% of registration fee
- Cancellations before 15 Nov. 2019 will be subject to charge 50% of registration fee
- No refund will be made for cancellations after 16 Nov. 2019
- No refund will be provided if the delegate is unable to attend the conference because a visa is not issues
- No refund will be provided if the delegate is unable to attend the conference because of travel delays or missed flights.
- No refunds will be provided for no-shows.