相關網址:https://cvgip2024.csie.ndhu.edu.tw/news/best-paper/

主講人：Prof. Jing-Ming Guo
1.Distinguished Professor, National Taiwan University of Science and Technology
2.Chief Technology Officer, Industrial Technology Research Institute
題　目：Development Trends and Considerations for Practical Applications of Generative AI
時 間：113年05月24日(五)下午1點20分至3點
地 點：台達館104教室

Title of speech
This presentation explores the latest trends in Generative AI, focusing on key topics such as the evolution of GPT, domain-specific optimization in large language models (LLMs), micro-sizing LLMs, and the deployment of Generative AI in Industrial Technology Research Institute (ITRI) and its potential expansion to Taiwan enterprises. The talk delves into the advancements in GPT models, highlights the significance of expert-level optimization techniques in enhancing LLM performance, and discusses strategies for scaling down large models. Moreover, it showcases the current efforts by ITRI in implementing Generative AI and outlines the future prospects of adopting this technology within Taiwan's business landscape.
Biography
Prof. Guo is currently a full Professor with the Department of Electrical Engineering, and Director of Advanced Intelligent Image and Vision Technology Research Center. He was Vice Dean of the College of Electrical Engineering and Computer Science, and Director of the Innovative Business Incubation Center, Office of Research and Development. His research interests include Big data signal processing, artificial intelligence, digital image/video processing, computer vision. Dr. Guo is a Senior Member of the IEEE and Fellow of the IET.
Dr. Guo is Chapter Chair of IEEE Signal Processing Society, Taipei Section, Board of Governor member of Asia Pacific Signal and Information Processing Association, and Chair of IET Taipei Local Network. He was General Chair of many international conferences, e.g., APSIPA 2023, IEEE Life Science Workshop 2020, ISPACS 2019, IEEE ICCE-Berlin 2019, IWAIT 2018, and IEEE ICCE-TW 2015. He was Technical Program Chair of many international conferences as well, e.g., IEEE ICIP 2023, IWAIT 2022, IEEE ICCE-TW 2014, IEEE ISCE 2013, and ISPACS 2012. He is/was Associate Editor of the IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technologies, IEEE Transactions on Multimedia, IEEE Signal Processing Letters, Information Sciences, Signal Processing, and Journal of Information Science and Engineering.

主講人：謝君偉教授（陽明交通大學）
題　目：Pre-trained Models與LLM結合測試
時 間：113年05月21日(二)上午10點10分至12點
地　點：台達館102教室

Abstract:
Large Language Models (LLMs) have demonstrated incredible potential in generating text that resembles human writing, as shown in numerous studies. Despite their impressive abilities, training them from scratch is not easy. Recently, model reuse has become a key technique, allowing developers to utilize pre-trained models, ranging from small ones to large language models, to improve the performance and efficiency of their machine learning systems. This presentation will explore how to establish connections between the widely adopted practice of model reuse across different domains and covers various stages of leveraging Pre-Trained Models (PTMs), including data preparation, architecture design, model training, and model inference. In addition to providing a comprehensive overview of typical methods for model reuse, we'll address the important issue of how to retrain a model using cost-effective GPUs like Nvidia 4090. We'll also discuss solutions and results based on Phison’s aiDAPTIV platform.

主講人：李承緒 副總 (訊連科技)
題　目：電腦視覺在業界的開發實務與應用
時 間：113年05月17日(五)下午1點20分至3點　
地　點：台達館104教室

Abstract:
近幾年來AI與AR技術突飛猛進，許多技術已經應用在日常生活上了，尤其是生成式AI更是帶來新技術的大躍進及應用的大爆發，因此也造就了許多商機。
訊連科技與玩美移動兩家軟體服務公司，分別投入了許多相關的研究與開發，本次演講將以技術與應用的層面介紹這兩家公司在AI與AR的研究開發成果。

Speaker：
李承緒，於1996年台大資訊工程系碩士畢業，目前於訊連科技擔任研發部資深副總，帶領超過兩百位工程師研發AI在多媒體及電腦視覺的技術與應用。

主講人：孔祥重 院士
題　目：Be a Thinker: Prepare for the New Era of Generative AI
時 間：113年04月30日(二)下午1點00分至2點10分
地　點：台達館104教室


Abstract:
We are in the beginning of the generative AI era where much of our society will undergo fundamental changes. There will be many new opportunities for everyone, but also lots of ncertainties that could be difficult to understand. Being able to think through issues deeply would be more important than ever. I would encourage practicing thinking and suggest some techniques.
Speaker：
H. T. Kung is William H. Gates Professor of Computer Science and Electrical Engineering at Harvard University. He conducts research on topics related to the application of artificial intelligence in manufacturing and healthcare, AI accelerators, VLSI design, high-performance computing, parallel and distributed computing, computer architectures, and computer networks.

主講人：Dr. Tsung-Wei Ke (CMU)
題　目：Learning Visual Perception that Foundation Models Haven’t Learned
時 間：113年01月08日(一)下午13點30分至14點30分
地　點：台達館613會議室

Abstract:
Foundation models have achieved significant success in computer vision research. Vision-Language Models (VLMs) seem promising to address recognition challenges. VLMs excel at picking out fine-grained semantics, including those unseen during training. The Segment Anything Model (SAM) is a strong and general image parser, which generates segmentation masks of any given prompt. However, these models have not fully solved visual perception. Recognition models, including VLMs, are not robust to distribution drifts, caused by factors such as occlusion, sensorial noise, and domain gap. Segmentation models, including SAM, fail to discover and localize parts from the whole in the image. Pixel groupings are often inconsistent at different segmentation granularities. In this talk, I will present our recent works that address both challenges. First, we combined the state-of-the-art image generative models and recognition models. By optimizing the recognition models with the generative objectives, we improved the recognition of out-of-distribution images. Lastly, we introduced unsupervised hierarchical image segmentation frameworks, which generate consistent pixel groupings across segmentation hierarchy.

Bio:
Tsung-Wei is a postdoctoral researcher at CMU, working with Katerina Fragkiadaki. He obtained his Ph.D degree from UC Berkeley, working with Stella Yu. He is interested in the field of computer vision and embodied AI.

Topic: Recent results on learning with diffusion models
Speaker: Ming-Hsuan Yang
Time: 112/12/25(Mon.)10:00-11:30
Place: Delta R106
Abstract:
Diffusion models have been successfully applied to text-to-image generation with state-of-the-art performance. In this talk, I will discuss how these models can be used for low-level vision tasks and 3D scenes. First, I will present our findings on exploiting features from diffusion models and transformers for zero-shot semantic correspondence and other applications. Next, I will describe how we exploit diffusion models as effective prior for dense prediction, such as surface normal, depth, and segmentation. I will then discuss how diffusion models can f a c i l i t a t e a r t i c u l a t e d 3 D reconstruction, 3D scene generation, and novel view synthesis. When time allows, I will present other results on fine-grained text-to-image generation and pixel-wise visual grounding of large multimodal models.

Bio:
Ming-Hsuan Yang is a Professor at UC Merced and a Research Scientist with Google. He received the Google Faculty Award in 2009 and CAREER Award from the National Science Foundation in 2012. Yang received paper awards at UIST 2017, CVPR 2018, ACCV 2018, and Longuet-Higgins Prize in CVPR 2023.
He is an Associate Editor-in-Chief of PAMI, Editor-in-Chief of CVIU, and Associate Editor of IJCV. He was the Program Chair for ACCV 2014 and ICCV 2019 and Senior Area Chair/Area Chair for CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, IJCAI, and AAAI. Yang is a Fellow of the IEEE and ACM.

主講人：Dr. Jonnie Penn (Harvard University)
題　目：What the History of AI Says about its Future: Designing with Non-Use in Mind
時 間：112年12月15日(五)上午10點00分至12點00分
地　點：台達館108教室
Abstract:
What the History of AI Says about its Future: Designing with Non-Use in Mind
Since the term ‘AI’ was introduced in the 1950s, it has been used to describe three entirely different schools of thought about the nature of machine intelligence. Why is this? This talk introduces the forgotten forces behind the origins of ‘AI.' These complex histories provide rich evidence with which to calibrate speculation about AI and AI Ethics in the decades ahead. I introduce one overlooked trend around non-use.
Questions for the students to think about in advance of the class:
How would knowing the intention of your user help you to design an AI system?
How could restraint (when a user chooses not to use a technology) help you design better AI systems?

Bio:
Dr Jonnie Penn, FRSA, is an Associate Teaching Professor of AI Ethics and Society at the University of Cambridge. He is a historian of technology, a #1 New York Times bestselling author, and public speaker.
Penn serves as a Faculty Affiliate at the Berkman Klein Center at Harvard University, a Research Fellow and Teaching Associate at the Department of History and Philosophy of Science, a Research Fellow at St. Edmund’s College and as an Associate Fellow at the Leverhulme Centre for the Future of Intelligence.
He was formerly a MIT Media Lab Assembly Fellow, Google Technology Policy Fellow, Fellow of the British National Academy of Writing and popular broadcaster.

主講人：鄭元博 副理 (聯詠科技)
題　目：電腦視覺的應用與挑戰—以iPhone電影模式為例
時 間：112年12月14日(四)下午3點30分至5點00分
地　點：台達館108教室

Abstract:
• 藉由實際範例，介紹電腦視覺演算法，如何應用於攜帶式產品上。
• 嘗試讓同學了解，如何將所學課程與業界需求進行連結。

學/經歷：
學歷：
• 國立清華大學資訊工程博士
經歷：
• 2013-2018，工研院資通所工程師。
• 2018至今，聯詠科技副理，負責視訊處理演算法開發。
目前帶領20人的演算法團隊，負責規劃事業部的新技術、新產品發展方向。

主講人：Prof. Xiaoming Liu (Michigan State University)
題　目：Computer Vision: A Journey of Pursuing 3D World Understanding
時 間：112年12月07日(四)下午15點30分至17點30分
地　點：台達館108教室
Abstract: We are living in a 3D world. When a camera takes a picture or video, many of the 3D information inevitably get lost due to the camera projection. As one of the most active fields in AI, computer vision aims to develop algorithms that can derive meaningful information from the visual content. One fundamental quest of computer vision is to recover the 3D information, and thus enables a faithful 3D understanding of the world through the lens of the camera. In this talk, I will share some of our experiences in pursuing the 3D world understanding, addressing problems such as 3D reconstruction, 3D detection, depth estimation, velocity estimation, etc. The solutions to these problems have been applied to applications including biometrics, autonomous driving, and digital human/face. In the end, I will also briefly overview other research efforts in the Computer Vision Lab at Michigan State University, such as AIGC for vision tasks, anti-deepfake, anti-spoofing, etc.
Bio: Dr. Xiaoming Liu is the MSU Foundation Professor, and Anil and Nandita Jain Endowed Professor at the Department of Computer Science and Engineering of Michigan State University (MSU). He received Ph.D. degree from Carnegie Mellon University in 2004. He works on computer vision, machine learning, and biometrics especially on face related analysis and 3D vision. Since 2012 he helps to develop a strong computer vision area in MSU who is ranked top 15 in US according to the 5-year statistics at csrankings.org. He has been Area Chairs for numerous conferences, the Co-Program Chair of BTAS’18, WACV’18, IJCB’22 and AVSS’22 conferences, and Co-General Chair of FG’23 conference. He is an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He has authored more than 200 scientific publications, and has filed 29 U.S. patents. His work has been cited over 20000 times according to Google Scholar, with an H-index of 73. He is a fellow of The Institute of Electrical and Electronics Engineers (IEEE) and International Association for Pattern Recognition (IAPR).