Explainable, Robust, and Universal: Towards Human-like Visual Scene Understanding

Speaker:Dr. Long CHEN
        School of Engineering and Applied Science
        Columbia University


Title: "Explainable, Robust, and Universal: Towards Human-like Visual
        Scene Understanding"

Date:   Thursday, 29 September 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 decade, deep learning has revolutionized computer vision,
multimedia, natural language processing, and all other artificial
intelligence sub-fields. Nowadays, these well-trained deep learning models
can significantly outperform our humans in many computer vision tasks,
including complex visual scene understanding (e.g., visual-language
tasks). Despite unprecedented attention and great success, today's visual
scene understanding models still fail to realize human-like understanding.
By "human-like", we mean that these vision systems should be equipped with
three types of abilities: 1) Explainable: The model should rely on (right)
explicit evidences when making decisions. 2) Robust: The model should be
robust to some situations with only "low-quality" training data. 3)
Universal: The model design is relatively universal, and it is expected to
be effective for various tasks or architectures. In this talk, I will show
my previous contributions in these directions and introduce some ongoing
and future plans towards next-generation human-like AI and computer vision
systems.


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Biography:

Dr. Long Chen is currently a postdoctoral research scientist at the School
of Engineering and Applied Science, Columbia University, USA. He obtained
his Ph.D. degree in Computer Science from Zhejiang University in 2020. His
primary research interests are computer vision, multimedia, and machine
learning. His research work has won several awards and honors, e.g., the
2020 Best Ph.D. Thesis of Zhejiang University and Zhejiang Province, the
2021 Global Top-100 Chinese Rising Stars in AI, the Best Paper Award of
the HUMA workshop in ACM MM 2021. His team has won the first place in the
International Video Relation Understanding Grand Challenge in 2021.