Wed
Nov 22
2023
Nov 22
2023
11/24(五)__開源與人工智慧發展：法律角度的觀察_主講人：侯宜秀 律師 (台灣人工智慧學校秘書長)
09:36 AM
webman
webman
主講人：侯宜秀 律師 (台灣人工智慧學校秘書長)
題 目：開源與人工智慧發展：法律角度的觀察
時 間：112年11月24日(五)上午10點10分至12點00分
地 點：台達館108教室
Bio:
Secretary General, Taiwan Ai Academy Foundation. Isabel Hou is a seasoned attorney focusing on technological innovation and intellectual property law and has served as a legal counsel for various government programs, prestigious companies, and NGOs in Taiwan since 2000. Isabel became a solo practitioner after leaving Lee and Li, attorneys-at-law, however, collaborates with fellow lawyers and professionals from various backgrounds on a project basis regularly ever since.
She is currently the Taiwan AI Academy Secretary General and is leading the AI Civic Forum project. Isabel served as a committee member of Taiwan’s Open Parliament Multi-stakeholder Forum from 2019-2022.
題 目：開源與人工智慧發展：法律角度的觀察
時 間：112年11月24日(五)上午10點10分至12點00分
地 點：台達館108教室
Bio:
Secretary General, Taiwan Ai Academy Foundation. Isabel Hou is a seasoned attorney focusing on technological innovation and intellectual property law and has served as a legal counsel for various government programs, prestigious companies, and NGOs in Taiwan since 2000. Isabel became a solo practitioner after leaving Lee and Li, attorneys-at-law, however, collaborates with fellow lawyers and professionals from various backgrounds on a project basis regularly ever since.
She is currently the Taiwan AI Academy Secretary General and is leading the AI Civic Forum project. Isabel served as a committee member of Taiwan’s Open Parliament Multi-stakeholder Forum from 2019-2022.
Wed
Nov 08
2023
Nov 08
2023
11/10(五)_Toward Foundation AI Models in Smart Manufacturing_主講人：陳維超 數位長暨資深副總經理 (英業達股份有限公司)
04:29 PM
webman
webman
主講人：陳維超 數位長暨資深副總經理 (英業達股份有限公司)
題 目：Toward Foundation AI Models in Smart Manufacturing
時 間：112年11月10日(五)上午10點10分至12點00分
地 點：台達館108教室
Abstract
The primary challenges of using AI in smart manufacturing include scope change, verification difficulty, and transfer quality. Ill-defined requirements often result in shifts in data collection scopes and concepts. The rarity of real manufacturing failures poses problems in verifying model quality. The need to scale out model deployment also implies stringent conditions for domain transfer. This talk discusses our recent progress and observations in these respective areas. In particular, we focus on technologies regarding the trustworthy exchange of datasets, which lays the foundations for models that can be widely applicable to various application scenarios in robotics, contactless sensing, and visual inspection.
Short Bio:
Wei-Chao Chen is the Chief Digital Officer and Senior Vice President at Inventec Corp., a tier-one electronics company, and the Chairman at Skywatch Innovation, a developer for cloud-based IoT and video products. Dr. Chen is also a Visiting Professor at the National Taiwan University. His research interests include graphics hardware, computational photography, augmented reality, and computer vision. Dr. Chen was the Chief AI Advisor at Inventec between 2018-2020, an adjunct faculty at the National Taiwan University between 2009-2018, a senior research scientist in Nokia Research Center at Palo Alto between 2007-2009, and a 3D Graphics Architect in NVIDIA between 2002-2006. Dr. Chen received his MS in Electrical Engineering from National Taiwan University (1996), and MS (2001) and Ph.D. (2002) in Computer Science from the University of North Carolina at Chapel Hill.
題 目：Toward Foundation AI Models in Smart Manufacturing
時 間：112年11月10日(五)上午10點10分至12點00分
地 點：台達館108教室
Abstract
The primary challenges of using AI in smart manufacturing include scope change, verification difficulty, and transfer quality. Ill-defined requirements often result in shifts in data collection scopes and concepts. The rarity of real manufacturing failures poses problems in verifying model quality. The need to scale out model deployment also implies stringent conditions for domain transfer. This talk discusses our recent progress and observations in these respective areas. In particular, we focus on technologies regarding the trustworthy exchange of datasets, which lays the foundations for models that can be widely applicable to various application scenarios in robotics, contactless sensing, and visual inspection.
Short Bio:
Wei-Chao Chen is the Chief Digital Officer and Senior Vice President at Inventec Corp., a tier-one electronics company, and the Chairman at Skywatch Innovation, a developer for cloud-based IoT and video products. Dr. Chen is also a Visiting Professor at the National Taiwan University. His research interests include graphics hardware, computational photography, augmented reality, and computer vision. Dr. Chen was the Chief AI Advisor at Inventec between 2018-2020, an adjunct faculty at the National Taiwan University between 2009-2018, a senior research scientist in Nokia Research Center at Palo Alto between 2007-2009, and a 3D Graphics Architect in NVIDIA between 2002-2006. Dr. Chen received his MS in Electrical Engineering from National Taiwan University (1996), and MS (2001) and Ph.D. (2002) in Computer Science from the University of North Carolina at Chapel Hill.
Tue
Oct 31
2023
Oct 31
2023
11/3(五)_Generating Moral Machines_主講人:Prof. Shao-Man Lee (Miin Wu School of Computing, National Cheng Kung University)
11:50 AM
webman
webman
主講人：Prof. Shao-Man Lee (Miin Wu School of Computing, National Cheng Kung University)
題 目：Generating Moral Machines
時 間：112年11月03日(五)上午10點10分至12點00分
地 點：台達館108教室
Abstract
This speech investigates the capabilities of large language models, specifically GPT-3.5 in accurately representing human moral judgments across diverse cultures. Using the Moral Machine experiment as a testbed, her research examines GPT-3.5 ’s decision making under varying cultural contexts.
While results indicate GPT-3.5 exhibits some ability to approximate human moral inclinations, significant discrepancies remain compared to experimental data, especially regarding nuanced cultural preferences. Her research highlights challenges for AI in precisely replicating complex and variable human moral expression. It underscores the need for incorporating heterogeneous values into model training to better portray inclusive, multi-faceted global decision-making.
Short Bio:
Shao-Man Lee is an assistant professor who applies computational techniques to study socio legal issues. With a background in law, she utilizes natural language processing methods to elucidate topics ranging from judicial behavior to risk communication during the COVID-19 pandemic.
She is also involved in creating open datasets and models, such as Traditional Chinese legal named entity resources, to advance legal computation. Through an interdisciplinary approach that synthesizes law, social sciences, and AI, her research aims to provide data driven insights into legal and social phenomena.
題 目：Generating Moral Machines
時 間：112年11月03日(五)上午10點10分至12點00分
地 點：台達館108教室
Abstract
This speech investigates the capabilities of large language models, specifically GPT-3.5 in accurately representing human moral judgments across diverse cultures. Using the Moral Machine experiment as a testbed, her research examines GPT-3.5 ’s decision making under varying cultural contexts.
While results indicate GPT-3.5 exhibits some ability to approximate human moral inclinations, significant discrepancies remain compared to experimental data, especially regarding nuanced cultural preferences. Her research highlights challenges for AI in precisely replicating complex and variable human moral expression. It underscores the need for incorporating heterogeneous values into model training to better portray inclusive, multi-faceted global decision-making.
Short Bio:
Shao-Man Lee is an assistant professor who applies computational techniques to study socio legal issues. With a background in law, she utilizes natural language processing methods to elucidate topics ranging from judicial behavior to risk communication during the COVID-19 pandemic.
She is also involved in creating open datasets and models, such as Traditional Chinese legal named entity resources, to advance legal computation. Through an interdisciplinary approach that synthesizes law, social sciences, and AI, her research aims to provide data driven insights into legal and social phenomena.
Fri
Oct 20
2023
Oct 20
2023
10/31(二)_Music-conditioned pluralistic dancing and a multi-camera system_主講人:Prof. Sanghoon Lee (Yonsei University, Korea)
05:00 PM
webman
webman
主講人：Prof. Sanghoon Lee (Yonsei University, Korea)
題 目：Music-conditioned pluralistic dancing and a multi-camera system
時 間：112年10月31日(二)下午15點30分至17點30分
地 點：台達館108教室
Abstract
In this talk, I would like to present “music-conditioned pluralistic dancing” and what we have done in our lab. toward building a multi-camera system for future research. When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres. Therefore, they tend to return certain patterns of the dance genres that they are familiar with. What if artificial intelligence could be used to help choreographers blend dance genres by suggesting various dances, and one that matches their choreographic style? Numerous task-specific variants of autoregressive networks have been developed for dance generation. Yet, a serious limitation remains that all existing algorithms can return repeated patterns for a given initial pose sequence, which may be inferior. To mitigate this issue, we proposed MNET, a novel and scalable approach that can perform music-conditioned pluralistic dance generation synthesized by multiple dance genres using only a single model. Here, we learned a dance genre aware latent representation by training a conditional generative adversarial network leveraging Transformer architecture. After demonstration of the dancing, I would like to introduce the effort of our labs. for implementation of our multi-camera system. From the camera-system, we can expect numerous possibilities of developing core technologies in the fusion research areas of combining computer vision and computer graphics.
Short Bio:
Sanghoon Lee is a Professor at the EE Department, Yonsei University, Korea. His current research interests include image processing, computer vision, and graphics. He was an Associate Editor of the IEEE Trans. on Image Processing from 2010 to 2014. He served as a Guest Editor for the IEEE Trans. on Image Processing in 2013. He was the General Chair of the 2013 IEEE IVMSP Workshop. He has been serving as the Chair of the IEEE P3333.1 Working Group since 2011. He served as an Associate Editor for the IEEE SPL from 2014 to 2018, and a Senior Area Editor of the IEEE SPL from 2018 to 2022. He was the IEEE IVMSP/MMSP TC (2014–2019)/(2016–2021) and the IVM TC Chair of APSIPA from 2018 to 2019. He has been serving as an Associate Editor of IEEE Trans. on Multimedia and a member of the Senior Editorial Board of the IEEE Signal Processing Magazine from 2022. He is a Board of Governors member of APSIPA, and also an Editor in Chief of APSIPA News Letters.
題 目：Music-conditioned pluralistic dancing and a multi-camera system
時 間：112年10月31日(二)下午15點30分至17點30分
地 點：台達館108教室
Abstract
In this talk, I would like to present “music-conditioned pluralistic dancing” and what we have done in our lab. toward building a multi-camera system for future research. When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres. Therefore, they tend to return certain patterns of the dance genres that they are familiar with. What if artificial intelligence could be used to help choreographers blend dance genres by suggesting various dances, and one that matches their choreographic style? Numerous task-specific variants of autoregressive networks have been developed for dance generation. Yet, a serious limitation remains that all existing algorithms can return repeated patterns for a given initial pose sequence, which may be inferior. To mitigate this issue, we proposed MNET, a novel and scalable approach that can perform music-conditioned pluralistic dance generation synthesized by multiple dance genres using only a single model. Here, we learned a dance genre aware latent representation by training a conditional generative adversarial network leveraging Transformer architecture. After demonstration of the dancing, I would like to introduce the effort of our labs. for implementation of our multi-camera system. From the camera-system, we can expect numerous possibilities of developing core technologies in the fusion research areas of combining computer vision and computer graphics.
Short Bio:
Sanghoon Lee is a Professor at the EE Department, Yonsei University, Korea. His current research interests include image processing, computer vision, and graphics. He was an Associate Editor of the IEEE Trans. on Image Processing from 2010 to 2014. He served as a Guest Editor for the IEEE Trans. on Image Processing in 2013. He was the General Chair of the 2013 IEEE IVMSP Workshop. He has been serving as the Chair of the IEEE P3333.1 Working Group since 2011. He served as an Associate Editor for the IEEE SPL from 2014 to 2018, and a Senior Area Editor of the IEEE SPL from 2018 to 2022. He was the IEEE IVMSP/MMSP TC (2014–2019)/(2016–2021) and the IVM TC Chair of APSIPA from 2018 to 2019. He has been serving as an Associate Editor of IEEE Trans. on Multimedia and a member of the Senior Editorial Board of the IEEE Signal Processing Magazine from 2022. He is a Board of Governors member of APSIPA, and also an Editor in Chief of APSIPA News Letters.
Tue
Jul 18
2023
Jul 18
2023
7/21(五)_On the 2nd AI Wave: Toward Interpretable, Reliable, and Sustainable AI_主講人：郭宗杰院士（南加州大學教授）
08:32 AM
webman
webman
主講人：Dr. C.-C. Jay Kuo (郭宗杰教授)
(University of Southern California)
題 目：On the 2nd AI Wave: Toward Interpretable, Reliable, and Sustainable AI
時 間：112年7月21日(五)上午9點30分至12點00分
地 點：台達館103教室
Abstract
Rapid advances in artificial intelligence (AI) in the last decade have been primarily attributed to the wide applications of deep learning (DL) technologies. I view these advances as the first AI wave. There are concerns with the first AI wave. DL solutions are a black box (i.e., not interpretable) and vulnerable to adversarial attacks (i.e., unreliable). Besides, the high carbon footprint yielded by large DL networks is a threat to our environment (i.e., not sustainable). Many researchers are looking for an alternative solution that is interpretable, reliable, and sustainable. This is expected to be the second AI wave. To this end, I have conducted research on green learning (GL) since 2015. GL was inspired by DL. Low carbon footprints, small model sizes, low computational complexity, and mathematical transparency characterize GL. It offers energy-effective solutions in cloud centers and mobile/edge devices. It has three main modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) decision learning. GL has been successfully applied to a few applications. I will use deepfake video detection and blind image/video quality assessment as two examples to demonstrate the effectiveness and efficiency of the GL solutions.
Speaker
Dr. C.-C. Jay Kuo received his Ph.D. from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as William M. Hogue Professor, Distinguished Professor of Electrical and Computer Engineering and Computer Science, and Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is a Fellow of AAAS, ACM, IEEE, NAI, and SPIE and an Academician of Academia Sinica.
Dr. Kuo has received a few awards for his research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo was Editor-in-Chief for the IEEE Transactions on Information Forensics and Security (2012-2014) and the Journal of Visual Communication and Image Representation (1997-2011). He is currently the Editor-in-Chief for the APSIPA Trans. on Signal and Information Processing (2022-2023). He has guided 168 students to their Ph.D. degrees and supervised 31 istanbul escort postdoctoral research fellows.
(University of Southern California)
題 目：On the 2nd AI Wave: Toward Interpretable, Reliable, and Sustainable AI
時 間：112年7月21日(五)上午9點30分至12點00分
地 點：台達館103教室
Abstract
Rapid advances in artificial intelligence (AI) in the last decade have been primarily attributed to the wide applications of deep learning (DL) technologies. I view these advances as the first AI wave. There are concerns with the first AI wave. DL solutions are a black box (i.e., not interpretable) and vulnerable to adversarial attacks (i.e., unreliable). Besides, the high carbon footprint yielded by large DL networks is a threat to our environment (i.e., not sustainable). Many researchers are looking for an alternative solution that is interpretable, reliable, and sustainable. This is expected to be the second AI wave. To this end, I have conducted research on green learning (GL) since 2015. GL was inspired by DL. Low carbon footprints, small model sizes, low computational complexity, and mathematical transparency characterize GL. It offers energy-effective solutions in cloud centers and mobile/edge devices. It has three main modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) decision learning. GL has been successfully applied to a few applications. I will use deepfake video detection and blind image/video quality assessment as two examples to demonstrate the effectiveness and efficiency of the GL solutions.
Speaker
Dr. C.-C. Jay Kuo received his Ph.D. from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as William M. Hogue Professor, Distinguished Professor of Electrical and Computer Engineering and Computer Science, and Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is a Fellow of AAAS, ACM, IEEE, NAI, and SPIE and an Academician of Academia Sinica.
Dr. Kuo has received a few awards for his research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo was Editor-in-Chief for the IEEE Transactions on Information Forensics and Security (2012-2014) and the Journal of Visual Communication and Image Representation (1997-2011). He is currently the Editor-in-Chief for the APSIPA Trans. on Signal and Information Processing (2022-2023). He has guided 168 students to their Ph.D. degrees and supervised 31 istanbul escort postdoctoral research fellows.
Mon
Jun 05
2023
Jun 05
2023
06/09(五)_ Mobile AI Evolution for Multimedia_主講人：鄭嘉珉博士 (聯發科資深經理)
02:50 PM
webman
webman
主講人： 鄭嘉珉 博士
(Senior Manager of Computing and AI Technology Group at MediaTek)
題 目：Mobile AI Evolution for Multimedia
時 間：112年6月9日(五)下午13點20分至15點10分
地 點：台達館103教室
Abstract
At the end of the Multimedia course this semester, I would like to take this opportunity to share my personal experience of mobile AI evolution for multimedia in the semiconductor industry and IC design house. It is especially suitable for students who want to learn more about industry-related information, and apply the fundamental knowledge learned to future research or employment. The content will be divided into three parts, including 1) IT (Information Technology) Generational Change, 2) Mobile AI Technology Evolution, 3) Marching Toward Generative AI Era. This talk looks forward to helping students think about the pathway that suits them, especially for those who are choosing courses or research fields, and those who want to join the semiconductor industry after graduation.
Speaker
Chia-Ming Cheng is the Senior Manager of Computing and AI Technology Group at MediaTek. He is responsible for technology planning and management of AI applications on mobile phones, TV, AIoT, and AR/VR products. He is also responsible for industry-university cooperation and ecosystem engagement for advanced AI technologies. He has extensive experience in advancing camera and computing technology in semiconductor manufacturing. He received his M.S. and Ph.D. in Computer Science from National Tsing Hua University, Taiwan. istanbul escort His research interest mainly focuses on computer vision, machine learning, computational photography, 3D modeling and rendering.
(Senior Manager of Computing and AI Technology Group at MediaTek)
題 目：Mobile AI Evolution for Multimedia
時 間：112年6月9日(五)下午13點20分至15點10分
地 點：台達館103教室
Abstract
At the end of the Multimedia course this semester, I would like to take this opportunity to share my personal experience of mobile AI evolution for multimedia in the semiconductor industry and IC design house. It is especially suitable for students who want to learn more about industry-related information, and apply the fundamental knowledge learned to future research or employment. The content will be divided into three parts, including 1) IT (Information Technology) Generational Change, 2) Mobile AI Technology Evolution, 3) Marching Toward Generative AI Era. This talk looks forward to helping students think about the pathway that suits them, especially for those who are choosing courses or research fields, and those who want to join the semiconductor industry after graduation.
Speaker
Chia-Ming Cheng is the Senior Manager of Computing and AI Technology Group at MediaTek. He is responsible for technology planning and management of AI applications on mobile phones, TV, AIoT, and AR/VR products. He is also responsible for industry-university cooperation and ecosystem engagement for advanced AI technologies. He has extensive experience in advancing camera and computing technology in semiconductor manufacturing. He received his M.S. and Ph.D. in Computer Science from National Tsing Hua University, Taiwan. istanbul escort His research interest mainly focuses on computer vision, machine learning, computational photography, 3D modeling and rendering.
Wed
May 24
2023
May 24
2023
6/2 (五) Generative AI: Lighting the Way, Embracing the Opportunity 主講人: 花凱龍 博士 (台灣微軟首席技術長)
12:21 PM
webman
webman
主講人： 花凱龍 博士
(台灣微軟首席技術長)
題 目：生成式人工智慧: 洞燭先機，掌握契機
TOPIC Generative AI: Lighting the Way, Embracing the Opportunity
時 間：112年6月2日(五)下午13點20分至15點10分
地 點：台達館103教室
Abstract: 在這次演講中，我將分享生成式人工智慧（AI）與基礎模型的觀念和技術。深度學習和類神經網路的發展，促使生成式AI展現驚人的互動和創作能力，並在眾多領域大放異彩。另外，本次演講也將介紹微軟 Azure OpenAI 的高度安全整合應用服務，為生成式AI的應用提供可信賴的基礎。透過這次演講，讓我們攜手把握生成式AI的先機，共同激發未來創新的想像力。
(台灣微軟首席技術長)
題 目：生成式人工智慧: 洞燭先機，掌握契機
TOPIC Generative AI: Lighting the Way, Embracing the Opportunity
時 間：112年6月2日(五)下午13點20分至15點10分
地 點：台達館103教室
Abstract: 在這次演講中，我將分享生成式人工智慧（AI）與基礎模型的觀念和技術。深度學習和類神經網路的發展，促使生成式AI展現驚人的互動和創作能力，並在眾多領域大放異彩。另外，本次演講也將介紹微軟 Azure OpenAI 的高度安全整合應用服務，為生成式AI的應用提供可信賴的基礎。透過這次演講，讓我們攜手把握生成式AI的先機，共同激發未來創新的想像力。
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Tue
Jan 10
2023
Jan 10
2023
1/13 (五) A Technology Passage to Metaverse 主講人: 章建中 博士 (高通公司副總)
06:54 PM
webman
webman
主講人： 章建中 博士
(高通公司副總)
題 目：A Technology Passage to Metaverse
時 間：112年01月13日(五)下午14點至15點30分
地 點：台達館102教室
Abstract:
As metaverse carries various nuances, in this talk, I'd focus more on some essential, yet challenging technologies for us to materialize the dream of metaverse. Hopefully, through the interactive discussions, this talk can be ebullient and useful for the audience.
Bio:
Chienchung Chang received his B.S. degree from National Tsing Hua University, Hsin- chu, Taiwan (1982) and his M.S. and Ph.D. degrees from the University of California, San Diego, La Jolla, (1987 and 1991, respectively), all in electrical and computer engineering. Dr. Chang has worked with Qualcomm since 1991. Currently, Dr. Chang is the vice president of engineering at Qualcomm Technologies, where he serves as the department head of the Multimedia R&D and Standards group, with a major focus in forward- looking research in the fields of speech, umraniye escort
video, imaging, CV, and AI and extended reality (XR) (VR/AR) technologies.
(高通公司副總)
題 目：A Technology Passage to Metaverse
時 間：112年01月13日(五)下午14點至15點30分
地 點：台達館102教室
Abstract:
As metaverse carries various nuances, in this talk, I'd focus more on some essential, yet challenging technologies for us to materialize the dream of metaverse. Hopefully, through the interactive discussions, this talk can be ebullient and useful for the audience.
Bio:
Chienchung Chang received his B.S. degree from National Tsing Hua University, Hsin- chu, Taiwan (1982) and his M.S. and Ph.D. degrees from the University of California, San Diego, La Jolla, (1987 and 1991, respectively), all in electrical and computer engineering. Dr. Chang has worked with Qualcomm since 1991. Currently, Dr. Chang is the vice president of engineering at Qualcomm Technologies, where he serves as the department head of the Multimedia R&D and Standards group, with a major focus in forward- looking research in the fields of speech, umraniye escort
video, imaging, CV, and AI and extended reality (XR) (VR/AR) technologies.
Fri
Dec 30
2022
Dec 30
2022
1/5 (四) 深度學習於自駕車之應用 主講人: 林哲聰博士 (Chalmers University of Technology, Sweden)
11:25 AM
webman
webman
主講人：林哲聰 博士
(Chalmers University of Technology, Sweden)
題 目：深度學習於自駕車之應用
時 間：112年01月05日(四)下午15點30分至17點
地 點：台達館102教室
Abstract:
本演講內容會介紹物件偵測的基礎知識，現有物件偵測模型的極限以及物件偵測在自駕車之各種應用。此外，本演講還會介紹如何運用合成資料甚至是生成式對抗網路來產生多樣化的訓練資料，近年來對抗攻擊模型的進化已經能讓物件偵測模型失效，而這又會對自駕車產生怎麼樣的潛在危害呢?本演講的最後還會簡短說明業界對於AI電腦視覺工程師的能力具有怎麼樣的期待！
(Chalmers University of Technology, Sweden)
題 目：深度學習於自駕車之應用
時 間：112年01月05日(四)下午15點30分至17點
地 點：台達館102教室
Abstract:
本演講內容會介紹物件偵測的基礎知識，現有物件偵測模型的極限以及物件偵測在自駕車之各種應用。此外，本演講還會介紹如何運用合成資料甚至是生成式對抗網路來產生多樣化的訓練資料，近年來對抗攻擊模型的進化已經能讓物件偵測模型失效，而這又會對自駕車產生怎麼樣的潛在危害呢?本演講的最後還會簡短說明業界對於AI電腦視覺工程師的能力具有怎麼樣的期待！
Fri
Dec 30
2022
Dec 30
2022
1/3 (二) Applied Computer Vision - From Recent Research to Future Metaverse主講人: 鄭嘉珉博士 (聯發科技)
11:23 AM
webman
webman
<h1>asd</h1>主講人：鄭嘉珉 博士
題 目：Applied Computer Vision - From Recent Research to Future Metaverse
時 間：112年01月03日(二)下午15點30分至17點
地 點：台達館102教室
Abstract:
At the end of the computer vision course this semester, I would like to take this opportunity to share my personal experience of computer vision in the semiconductor industry and IC design house, especially for students who want to learn more about industry-related information and how to use the acquired CV/ML knowledge for future research or employment. The content will be divided into three parts, including 1) applied computer vision, 2) recent CV research trend, 3) marching toward metaverse era.
This talk will help students find more specific goals, especially for students who are choosing a research field or a thesis topic, and students who want to join the semiconductor industry after graduation.
Speaker:
Chia-Ming Cheng is the Senior Manager of Computing and Artificial intelligence Technology Group at MediaTek. He is responsible for technology planning and management of AI applications on mobile phones, TV, AIoT, and AR/VR products. He is also responsible for industry-university cooperation and ecosystem engagement for advanced AI technologies. He has extensive experience in advancing camera and computing technology in semiconductor manufacturing. He received his M.S. and Ph.D. in Computer Science from National Tsing Hua University, Taiwan. His research interest mainly focuses on computer vision, machine learning, computational photography, 3D modeling and rendering.
題 目：Applied Computer Vision - From Recent Research to Future Metaverse
時 間：112年01月03日(二)下午15點30分至17點
地 點：台達館102教室
Abstract:
At the end of the computer vision course this semester, I would like to take this opportunity to share my personal experience of computer vision in the semiconductor industry and IC design house, especially for students who want to learn more about industry-related information and how to use the acquired CV/ML knowledge for future research or employment. The content will be divided into three parts, including 1) applied computer vision, 2) recent CV research trend, 3) marching toward metaverse era.
This talk will help students find more specific goals, especially for students who are choosing a research field or a thesis topic, and students who want to join the semiconductor industry after graduation.
Speaker:
Chia-Ming Cheng is the Senior Manager of Computing and Artificial intelligence Technology Group at MediaTek. He is responsible for technology planning and management of AI applications on mobile phones, TV, AIoT, and AR/VR products. He is also responsible for industry-university cooperation and ecosystem engagement for advanced AI technologies. He has extensive experience in advancing camera and computing technology in semiconductor manufacturing. He received his M.S. and Ph.D. in Computer Science from National Tsing Hua University, Taiwan. His research interest mainly focuses on computer vision, machine learning, computational photography, 3D modeling and rendering.
Contact
- 賴尚宏老師
- 國立清華大學資訊工程學系電腦視覺實驗室
- 新竹市光復路二段101號台達館719,720,721
- 電話: (03)5715131 720:分機80932, 721:分機80933
- Dr. Shang-Hong Lai
- Computer Vision Lab
- Department of Computer Science, National Tsing Hua University
- Rooms 719,720,721 , Delta Building , No. 101, Section 2, Kuang-Fu Road, Hsinchu, Taiwan 30013, R.O.C.
- TEL: (03)5715131 720:#80932, 721:#80933
Directions
- 台達館 Delta Building
進入清大校園後直行，經過大草坪後右轉，再往前直走，經過教育館後，看到工三館左轉即可到達台達館 - GPS座標
北緯:24.79591 東經:120.99211