IJCAI is the International Joint Conference on Artificial Intelligence, the main international gathering of researchers in AI. The 26th International Joint Conference on Artificial Intelligence is held in Melbourne, Australia in August 19-26. PALM students Yi Ren, Zengwei Huo, Yu Song attend the conference and report their newly research.
Yi Ren is giving report(left) and sharing the post(right).
Zengwei Huo is giving report(left) and sharing the post(right).
Yu Song is giving report(left) and sharing the post(right).
The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) is held February 4–9 at the Hilton San Francisco, San Francisco, California, USA. PALM faulties Xin Geng and Min-ling Zhang and students Miao-gen Ling, Cai-zhi Tang and Hai-ming Xu attended this conference. Miao-gen Ling and Cai-zhi Tang gave a report on their newly research, meanwhile, Hai-ming Xu took part in the post communication.
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR2016) was held from June 26th to July 1st at Las Vegas, NV, USA. Proffessor Xin Geng and student Chao Xing attended the conference. Chao Xing showed the poster about the "Logistic Boosting Regression for Label Distribution Learning", moreover, he gave an oral talk about the CVPR2016 ChaLearn LAP and FoW - Age Estimation Competition named "Deep Age Distribution Learning for Apparent Age Estimation".
The use of music as an aid in improving body and mind has received enormous attention over the last 20 years from a wide range of disciplines, including neuroscience, physical therapy, exercise science, and psychological medicine. We have attempted to transform insights gained from the scientific study of music, learning, and medicine into real-life applications that can be delivered widely, effectively, and accurately. We have been using music to enhance learning as well as to augment evidence-based medicine. In this talk, I will describe tools to facilitate the delivery of established music-enhanced therapies, harnessing the synergy of sound and music computing (SMC), mobile computing, and cloud computing technologies to promote learning and to facilitate disease prevention, diagnosis, and treatment in both developed countries and resource-poor developing countries. These tools are being developed as part of ongoing research projects that combine wearable sensors, smartphone apps, and cloud-based therapy delivery systems to facilitate music-enhanced learning and music-enhanced physical therapy. I will also discuss the joys and pains working in such a multidisciplinary environment.
Ye Wang is an Associate Professor in the Computer Science Department at the National University of Singapore (NUS) and NUS Graduate School for Integrative Sciences and Engineering (NGS). He established and directed the sound and music computing (SMC) Lab (www.smcnus.org). Before joining NUS he was a member of the technical staff at Nokia Research Center in Tampere, Finland for 9 years. His research interests include sound analysis and music information retrieval (MIR), mobile computing, and cloud computing, and their applications in music edutainment , e-Learning, and e-Health, as well as determining their effectiveness via subjective and objective evaluations. His most recent projects involve the design and evaluation of systems to support 1) therapeutic gait training using Rhythmic Auditory Stimulation (RAS), 2) second language learning, and 3) motivating exercise via music-based systems.
Congratulations to the PALM team 'palm_seu' which won the runner-up in the Chalearn LAP Apparent Age Estimation challenge!
The challenge is part of Face Analysis Workshop and Challenge, CVPR'16 and the details can be found in http://gesture.chalearn.org/ . The team is supervised by professor Xin Geng and its members include: Chao Xing, Zeng-wei Huo, Ying Zhou, Xu Yang, Peng Hou and Jia-qi Lv. The result of the challenge is showed in the table.
The Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16) was held February 12-17 at the Phoenix Convertion Center, Phoenix, Arizona, USA. Peng Hou attended the conference and gave an oral talk about the paper "Multi-Label Manifold Learning".
Text information appearing in a scene carries vital information for interpreting the contents, and identifying objects and surrounding environment in images. Although conventional document analysis techniques have bee quite successful, identifying general text in images remains a very challenging research problem. The recent prestigious conferences on computer vision, pattern recognition, and machine learning have seen an increased interest on this topic from the computer vision research community. In this talk, I will show you the challenges and the recent advances in research on scene text recognition and our solutions and attempts.
Dr. Wenjing Jia is currently a Lecturer at the School of Computing and Communications at University of Technology Sydney (UTS) teaching various under- and postgraduate internetworking subjects. She is also a core research member of UTS Global Big Data Technologies Centre. Her research interests include image processing/analysis and object detection and recognition. In particular, she has been working in the field of text information extraction for several years. This has included working on applications such as vehicle identification via recognizing their license plates, textual information retrieval from images on web pages and emails, and text sign recognition from natural scene images. She has had over 70 publications in journals such as TIP and conferences such as ICIP and ICPR. A focus of more recent work has been to explore deep features and deep learning architectures for detecting and recognising scene text or text signage from unconstrained, outdoor street level imagery. Prior to UTS, Wenjing worked at Fuzhou University from 1999 to 2003 as an Associate Lecturer teaching various subjects in communications and information systems and conducting research on medical image analysis.
Congratulations to the PALM graduates Lei Wu who won the Jiangsu Province outstanding master's degree thesis!
个人简介：中科院自动化所模式识别国家重点实验室研究员、副主任，中科院脑科学与智能技术卓越创新研究中心核心骨干，中国科学院大学首席教授，国家杰出青年基金获得者。担任中国计算机学会常务理事，中国人工智能学会理事，中国中文信息学会理事，中国声学学会理事，Steering Committee Member of IEEE Trans. On Affective Computing，以及Journal on Multimodal User Interfaces， Speech Communication等期刊编委。主要研究方向为语音识别与合成、人机交互、模式识别等，在主要国际期刊或会议上发表论文100余篇。