Towards Smart, Efficient, and Reliable Semantic Image Recognition

Speaker:Dr. Dong Zhang
        Vision and System Design Lab
        The Hong Kong University of Science and Technology

Title:  "Towards Smart, Efficient, and Reliable Semantic
         Image Recognition"

Date:   19 October, 2022

Time:   10:00am - 11:00am HKT

Zoom link:
https://hkust.zoom.us/j/465698645?pwd=aVRaNWs2RHNFcXpnWGlkR05wTTk3UT09

Meeting ID:     465 698 645
Passcode:       20222023

Abstract:

Over the past years, semantic image recognition (e.g., image
classification, object detection, and semantic segmentation) has achieved
fantastic progress based on deep learning technologies. However, for
demanding applications which require ultra-high accuracy and/or real-time
performance such as autonomous driving and medical image analysis, there
is still significant room for improvement. To address such challenges, a
key to success is to equip the machine learning models with rich yet
discriminative feature representations. In light of this context, the goal
of my research strives to build smart, efficient, and reliable computer
vision systems that can recognize and understand complex visual scenarios.
In this talk, I will first introduce my previous contributions in these
directions and their applications. Then, I will introduce some ongoing and
future research plans for next-generation computer vision systems.


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Biography:

Dr. Dong Zhang is currently a postdoctoral researcher at the Vision and
System Design Lab, The Hong Kong University of Science and Technology,
working with Prof. Kwang-Ting Cheng. He obtained his Ph.D. degree in
Computer Science and Technology from Nanjing University of Science and
Technology in Jan. 2022. From Sep. 2018 to Sep. 2020, he was supported by
China Scholarship Council as a joint Ph.D. student at Nanyang
Technological University. His primary research interests are in machine
learning, computer vision, and medical image analysis, especially some
fundamental research tasks such as image classification, semantic
segmentation, object detection, and their practical applications.