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Weakly Semantic Guided Action Recognition
Tingzhao Yu1,2; Lingfeng Wang1; Cheng Da1,2; Huxiang Gu1; Shiming Xiang1; Chunhong Pan1
Source PublicationIEEE Transactions on Multimedia

Action recognition plays a fundamental role in computer vision and video analysis. Nevertheless, extracting effective spatial-temporal features remains a challenging task. This paper proposes three simple but effective weakly Semantic Guided Modules (SGMs) for both environment constrained and cross domain action recognition. The SGMs are composed of totally 3D convolution and element-wise gated operations, thus they are efficient and easy to be implemented. The semantic guidance is obtained in a weakly supervised manner, in which each video clip is labeled with only an action class instead of pixel level semantics. Benefitting from the semantic guidance, the network (named SGN, abbreviation for Semantic Guided Network) can focus on the salient parts of the video clips. Consequently, the redundant information can be reduced and the model is more robust to noises. Besides, benefitting from the intrinsic property of SGMs, SGN is totally end-to-end trainable. Quantities of experiments on both environment constrained (e.g., Penn, HMDB-51 and UCF-101) and cross domain (e.g., ODAR) action recognition datasets demonstrate its effectiveness. Specifically, SGN gets improvements of 3:7%, 2:1% and 5:2% for Penn, HMDB-51 and UCF-101 than the baseline ResNet3D, respectively, and SGN ranked the 3rd place in ODAR 2017 challenge.

KeywordSemantic Guided Module Action Recognition Cross Domain 3d Convolution Attention Model
Document Type期刊论文
Corresponding AuthorTingzhao Yu
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.School of Computer and Control Engineering, University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Tingzhao Yu,Lingfeng Wang,Cheng Da,et al. Weakly Semantic Guided Action Recognition[J]. IEEE Transactions on Multimedia,2019,1(1):1-14.
APA Tingzhao Yu,Lingfeng Wang,Cheng Da,Huxiang Gu,Shiming Xiang,&Chunhong Pan.(2019).Weakly Semantic Guided Action Recognition.IEEE Transactions on Multimedia,1(1),1-14.
MLA Tingzhao Yu,et al."Weakly Semantic Guided Action Recognition".IEEE Transactions on Multimedia 1.1(2019):1-14.
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