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Extreme vocabulary learning

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Abstract

Regarding extreme value theory, the unseen novel classes in the open-set recognition can be seen as the extreme values of training classes. Following this idea, we introduce the margin and coverage distribution to model the training classes. A novel visual-semantic embedding framework — extreme vocabulary learning (EVoL) is proposed; the EVoL embeds the visual features into semantic space in a probabilistic way. Notably, we adopt the vast open vocabulary in the semantic space to help further constraint the margin and coverage of training classes. The learned embedding can directly be used to solve supervised learning, zero-shot learning, and open set recognition simultaneously. Experiments on two benchmark datasets demonstrate the effectiveness of the proposed framework against conventional ways.

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Author information

Author notes

  1. These authors contributed equally to this work.

Authors and Affiliations

  1. School of Data Science, Fudan University, Shanghai, 200433, China

    Hanze Dong & Yanwei Fu

  2. School of Computer Science, Fudan University, Shanghai, 201203, China

    Zhenfeng Sun, Shi Zhong & Yu-Gang Jiang

  3. Department of Statistics, University of Wisconsin, Madison, 53706, USA

    Zhengjun Zhang

Authors
  1. Hanze Dong
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  2. Zhenfeng Sun
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  3. Yanwei Fu
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  4. Shi Zhong
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  5. Zhengjun Zhang
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  6. Yu-Gang Jiang
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Corresponding author

Correspondence to Yanwei Fu.

Additional information

Hanze Dong is an undergraduate student majoring in mathematics and applied mathematics (data science track) at the School of Data Science, Fudan University, China. He works in Shanghai Key Lab of Intelligent Information Processing under the supervision of Professor Yanwei Fu. His current research interests include both machine learning theory and its applications.

Zhenfeng Sun is a DEng student in the School of Computer Science, Fudan University, China. He received his master degree in Computer Science Department of Tongji University, China in 2003. He had many years of working experience in the video industry. In 2005, he was responsible for deploying Alcatel Lucent’s leading triple play solution in China. In 2013, he joined the world’s largest IPTV operator, BESTV Company. He was responsible for terminal department and participated in a national key research project. He is now the co-founder of OTVClOUD company invested by Yunfeng Fund (set up by Jack Ma, founder of Alibaba). His research interests are video retrieval, recognition and innovative video applications. He participated in and obtained several patents.

Yanwei Fu received the PhD degree from Queen Mary University of London, UK in 2014, and the MEng degree from the Department of Computer Science and Technology, Nanjing University, China in 2011. He held a post-doctoral position at Disney Research, Pittsburgh, PA, USA, from 2015 to 2016. He is currently a tenure-track professor with Fudan University, China. His research interests are image and video understanding, and life-long learning.

Shi Zhong received PhD degree in 2018 at the School of Computer Science, Fudan University, China. His research interests mainly include computer vision and machine learning.

Zhengjun Zhang is full professor of Statistics in the Department of Statistics at University of Wisconsin-Madison, USA. He received his PhD degrees in Management Engineering and Statistics from Beihang University and the University of North Carolina at Chapel Hill, respectively. Dr. Zhang’s main research areas include the Big Data structure and inference, particularly in extreme value analysis for interdependent critical risk variables in finance, climate, and medical sciences, in stochastic optimizations in large and complex systems. Some of his selected journal publications are Annals of Statistics, Journal of Royal Statistical Society, Series B, Journal of American Statistical Association, Journal of Econometrics, Journal of Banking and Finance, Extremes, and Automatica.

Yu-Gang Jiang is professor of Computer Science at Fudan University and Director of Fudan-Jilian Joint Research Center on Intelligent Video Technology, China. He is interested in all aspects of extracting high-level information from big video data, such as video event recognition, object/scene recognition and largescale visual search. His work has led to many awards, including the inaugural ACM China Rising Star Award, the 2015 ACM SIGMM Rising Star Award, and the research award for outstanding young researchers from NSF China. He is currently an associate editor of ACM TOMM, Machine Vision and Applications (MVA) and Neurocomputing. He holds a PhD in Computer Science from City University of Hong Kong and spent three years working at Columbia University before joining Fudan in 2011.

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Dong, H., Sun, Z., Fu, Y. et al. Extreme vocabulary learning. Front. Comput. Sci. 14, 146315 (2020). https://doi.org/10.1007/s11704-019-8249-3

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