[Theory] NOW: 2/23 Talks at TTIC: Shu Kong, Carnegie Mellon University

[Mary Marre]

*When:*        Wednesday, February 23rd at* 11:30 am CT*


*Where:*       Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_yKWbvEQwQeCV4yigQ9Uwbg>*)


*Who: *         Shu Kong, Carnegie Mellon University


*Title:          *Open-World Visual Perception

*Abstract: *Visual perception is indispensable in numerous applications
such as autonomous vehicles. Today's visual perception algorithms are often
developed under a closed-world paradigm, which assumes the data
distribution and categorical labels are fixed a priori. This assumption is
unrealistic in the real open world, which contains situations that are
dynamic and unpredictable. As a result, closed-world visual perception
systems appear to be brittle in the open-world. For example, autonomous
vehicles with such systems could fail to recognize a never-before-seen
overturned truck and crash into it. We are motivated to ask how to (1)
detect all the object instances in the image, and (2) recognize the
unknowns. In this talk, I will present my solutions and introduce more
research topics in the direction of Open-World Visual Perception.

*Bio: *Shu Kong is a Postdoctoral Fellow in the Robotics Institute at
Carnegie-Mellon University, supervised by Prof. Deva Ramanan. He earned a
Ph.D. in Computer Science at the University of California-Irvine, advised
by Prof. Charless Fowlkes. His research interests span computer vision and
machine learning, and their applications to autonomous vehicles and natural
science. His current research focuses on Open-World Visual Perception. His
recent paper on this topic received Best Paper / Marr Prize Honorable
Mention at ICCV 2021. He regularly serves on the program committee in major
conferences of computer vision and machine learning. He also serves as the
lead organizer of workshops on Open-World Visual Perception at CVPR 2021
and 2022. His latest interdisciplinary research includes building a
high-throughput pollen analysis system, which was featured by the National
Science Foundation as that "opens a new era of fossil pollen research".

*H**ost:* *Greg Shakhnarovich* <greg at ttic.edu>


Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Wed, Feb 23, 2022 at 10:34 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Wednesday, February 23rd at* 11:30 am CT*
>
>
> *Where:*       Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_yKWbvEQwQeCV4yigQ9Uwbg>*
> )
>
>
> *Who: *         Shu Kong, Carnegie Mellon University
>
>
> *Title:          *Open-World Visual Perception
>
> *Abstract: *Visual perception is indispensable in numerous applications
> such as autonomous vehicles. Today's visual perception algorithms are often
> developed under a closed-world paradigm, which assumes the data
> distribution and categorical labels are fixed a priori. This assumption is
> unrealistic in the real open world, which contains situations that are
> dynamic and unpredictable. As a result, closed-world visual perception
> systems appear to be brittle in the open-world. For example, autonomous
> vehicles with such systems could fail to recognize a never-before-seen
> overturned truck and crash into it. We are motivated to ask how to (1)
> detect all the object instances in the image, and (2) recognize the
> unknowns. In this talk, I will present my solutions and introduce more
> research topics in the direction of Open-World Visual Perception.
>
> *Bio: *Shu Kong is a Postdoctoral Fellow in the Robotics Institute at
> Carnegie-Mellon University, supervised by Prof. Deva Ramanan. He earned a
> Ph.D. in Computer Science at the University of California-Irvine, advised
> by Prof. Charless Fowlkes. His research interests span computer vision and
> machine learning, and their applications to autonomous vehicles and natural
> science. His current research focuses on Open-World Visual Perception. His
> recent paper on this topic received Best Paper / Marr Prize Honorable
> Mention at ICCV 2021. He regularly serves on the program committee in major
> conferences of computer vision and machine learning. He also serves as the
> lead organizer of workshops on Open-World Visual Perception at CVPR 2021
> and 2022. His latest interdisciplinary research includes building a
> high-throughput pollen analysis system, which was featured by the National
> Science Foundation as that "opens a new era of fossil pollen research".
>
> *H**ost:* *Greg Shakhnarovich* <greg at ttic.edu>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Tue, Feb 22, 2022 at 3:11 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*        Wednesday, February 23rd at* 11:30 am CT*
>>
>>
>> *Where:*       Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_yKWbvEQwQeCV4yigQ9Uwbg>*
>> )
>>
>>
>> *Who: *         Shu Kong, Carnegie Mellon University
>>
>>
>> *Title:          *Open-World Visual Perception
>>
>> *Abstract: *Visual perception is indispensable in numerous applications
>> such as autonomous vehicles. Today's visual perception algorithms are often
>> developed under a closed-world paradigm, which assumes the data
>> distribution and categorical labels are fixed a priori. This assumption is
>> unrealistic in the real open world, which contains situations that are
>> dynamic and unpredictable. As a result, closed-world visual perception
>> systems appear to be brittle in the open-world. For example, autonomous
>> vehicles with such systems could fail to recognize a never-before-seen
>> overturned truck and crash into it. We are motivated to ask how to (1)
>> detect all the object instances in the image, and (2) recognize the
>> unknowns. In this talk, I will present my solutions and introduce more
>> research topics in the direction of Open-World Visual Perception.
>>
>> *Bio: *Shu Kong is a Postdoctoral Fellow in the Robotics Institute at
>> Carnegie-Mellon University, supervised by Prof. Deva Ramanan. He earned a
>> Ph.D. in Computer Science at the University of California-Irvine, advised
>> by Prof. Charless Fowlkes. His research interests span computer vision and
>> machine learning, and their applications to autonomous vehicles and natural
>> science. His current research focuses on Open-World Visual Perception. His
>> recent paper on this topic received Best Paper / Marr Prize Honorable
>> Mention at ICCV 2021. He regularly serves on the program committee in major
>> conferences of computer vision and machine learning. He also serves as the
>> lead organizer of workshops on Open-World Visual Perception at CVPR 2021
>> and 2022. His latest interdisciplinary research includes building a
>> high-throughput pollen analysis system, which was featured by the National
>> Science Foundation as that "opens a new era of fossil pollen research".
>>
>> *H**ost:* *Greg Shakhnarovich* <greg at ttic.edu>
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Chicago, IL  60637*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>>
>> On Thu, Feb 17, 2022 at 12:51 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:*        Wednesday, February 23rd at* 11:30 am CT*
>>>
>>>
>>> *Where:*       Zoom Virtual Talk (*register in advance here
>>> <https://uchicagogroup.zoom.us/webinar/register/WN_yKWbvEQwQeCV4yigQ9Uwbg>*
>>> )
>>>
>>>
>>> *Who: *         Shu Kong, Carnegie Mellon University
>>>
>>>
>>> *Title:          *Open-World Visual Perception
>>>
>>> *Abstract: *Visual perception is indispensable in numerous applications
>>> such as autonomous vehicles. Today's visual perception algorithms are often
>>> developed under a closed-world paradigm, which assumes the data
>>> distribution and categorical labels are fixed a priori. This assumption is
>>> unrealistic in the real open world, which contains situations that are
>>> dynamic and unpredictable. As a result, closed-world visual perception
>>> systems appear to be brittle in the open-world. For example, autonomous
>>> vehicles with such systems could fail to recognize a never-before-seen
>>> overturned truck and crash into it. We are motivated to ask how to (1)
>>> detect all the object instances in the image, and (2) recognize the
>>> unknowns. In this talk, I will present my solutions and introduce more
>>> research topics in the direction of Open-World Visual Perception.
>>>
>>> *Bio: *Shu Kong is a Postdoctoral Fellow in the Robotics Institute at
>>> Carnegie-Mellon University, supervised by Prof. Deva Ramanan. He earned a
>>> Ph.D. in Computer Science at the University of California-Irvine, advised
>>> by Prof. Charless Fowlkes. His research interests span computer vision and
>>> machine learning, and their applications to autonomous vehicles and natural
>>> science. His current research focuses on Open-World Visual Perception. His
>>> recent paper on this topic received Best Paper / Marr Prize Honorable
>>> Mention at ICCV 2021. He regularly serves on the program committee in major
>>> conferences of computer vision and machine learning. He also serves as the
>>> lead organizer of workshops on Open-World Visual Perception at CVPR 2021
>>> and 2022. His latest interdisciplinary research includes building a
>>> high-throughput pollen analysis system, which was featured by the National
>>> Science Foundation as that "opens a new era of fossil pollen research".
>>>
>>> *H**ost:* *Greg Shakhnarovich* <greg at ttic.edu>
>>>
>>>
>>>
>>> Mary C. Marre
>>> Faculty Administrative Support
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue*
>>> *Chicago, IL  60637*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>
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