
Binary Neural Networks
Description
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Key Features
Reviews recent advances in CNN compression and acceleration
Elaborates recent advances on binary neural network (BNN) technologies
Introduces applications of BNN in image classification, speech recognition, object detection, and more
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Persons
Sheng Xu received the B.E. degree in Automotive Engineering from Beihang University, Beijing, China. He is currently a Ph.D. with the school of Automation Science and Electrical Engineering, Beihang University, Beijing, China, specializing in computer vision, model quantization, and compression. He has made significant contributions to the field and has published about a dozen papers as the first author in top-tier conferences and journals such as CVPR, ECCV, NeurIPS, AAAI, BMVC, IJCV, and ACM TOMM. Notably, he has 4 papers selected as oral or highlighted presentations by these prestigious conferences. Furthermore, Sheng Xu actively participates in the academic community as a reviewer for various international journals and conferences, including CVPR, ICCV, ECCV, NeurIPS, ICML, and IEEE TCSVT. His expertise has also led to his group's victory in the ECCV 2020 tiny object detection challenge.
Mingbao Lin finished his M.S.-Ph.D. study and obtained the Ph.D. degree in intelligence science and technology from Xiamen University, Xiamen, China, in 2022. Earlier, he received the B.S. degree from Fuzhou University, Fuzhou, China, in 2016. He is currently a senior researcher with the Tencent Youtu Lab, Shanghai, China. His publications on top-tier conferences/journals include IEEE TPAMI, IJCV, IEEE TIP, IEEE TNNLS, CVPR, NeurIPS, AAAI, IJCAI, ACM MM and so on. His current research interest is to develop efficient vision model, as well as information retrieval.
Tiancheng Wang received the B.E. degree in Automation from Beihang University, Beijing, China. He is currently pursuing the Ph.D. degree with the school of Institute of Artificial Intelligence, Beihang University, Beijing, China. During undergraduate, he has been awarded the title of Merit Student for several consecutive years, and has received various scholarships including academic excellence scholarship and academic competitions scholarship, etc. He was involved in several AI projects, including behavior detection and intention understanding research and unmanned air-based vision platform, etc. Now, his current research interests include deep learning and network compression, his goal is to explore the highly energy-saving model and drive the deployment of neural networks in embedded devices.
Dr. David Doermann is a Professor of Empire Innovation at the University at Buffalo (UB) and the Director of the University at Buffalo Artificial Intelligence Institute. Prior to coming to UB, he was a program manager at the Defense Advanced Research Projects Agency (DARPA), where he developed, selected and oversaw approximately $150 million in research and transition funding in the areas of computer vision, human language technologies and voice analytics. He coordinated performers on all of the projects, orchestrating consensus, evaluating cross team management and overseeing fluid program objectives.
Content
Chapter 2: Quantization of Neural Networks.
Chapter 3: Algorithms for Binary Neural Networks.
Chapter 4: Binary Neural Architecture Search.
Chapter 5: Applications in Natural Language Processing.
Chapter 6: Applications in Computer Vision.
Bibliography.
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