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Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies
Yang Wu1; Ding-Heng Wang2; Xiao-Tong Lu3; Fan Yang4; Man Yao2,5; Wei-Sheng Dong3; Jian-Bo Shi6; Guo-Qi Li7,8
Source PublicationMachine Intelligence Research

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs, particularly the modern deep neural networks (DNNs) and some brain-inspired methodologies, have largely boosted the recognition performance on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Although recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this survey, we present the review of recent advances with our suggestions on the new possible directions to wards improving the efficiency of DNN-related and brain-inspired visual recognition approaches, including efficient network compression and dynamic brain-inspired networks. We investigate not only from the model but also from the data point of view (which is not the case in existing surveys) and focus on four typical data types (images, video, points, and events). This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.

KeywordVisual recognition deep neural networks (DNNS) brain-inspired methodologies network compression dynamic inference survey
Sub direction classification其他
planning direction of the national heavy laboratory其他
Paper associated data
Chinese guide
Video parsing
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Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Collection学术期刊_Machine Intelligence Research
Affiliation1.Applied Research Center Laboratory, Tencent Platform and Content Group, Shenzhen 518057, China
2.School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi′an Jiaotong University, Xi′an 710049, China
3.School of Artificial Intelligence, Xidian University, Xi′an 710071, China
4.Division of Information Science, Nara Institute of Science and Technology, Nara 6300192, Japan
5.Peng Cheng Laboratory, Shenzhen 518000, China
6.Department of Computer and Information Science, University of Pennsylvania, Philadelphia PA 19104-6389, USA
7.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
8.University of Chinese Academy of Sciences, Beijing 100190, China
Recommended Citation
GB/T 7714
Yang Wu,Ding-Heng Wang,Xiao-Tong Lu,et al. Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies[J]. Machine Intelligence Research,2022,19(5):366-411.
APA Yang Wu.,Ding-Heng Wang.,Xiao-Tong Lu.,Fan Yang.,Man Yao.,...&Guo-Qi Li.(2022).Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies.Machine Intelligence Research,19(5),366-411.
MLA Yang Wu,et al."Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies".Machine Intelligence Research 19.5(2022):366-411.
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