.png){kind=icon}
Return
Deep snippet selective network for weakly supervised temporal action localization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2020.107686.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Temporal action localization has been a hot topic in video analyzation. In this paper, we propose a novel method called deep snippet selective network (DSSN) to address two key problems in weak supervision for temporal action localization, which are separability and integrality. Specifically, we employ two eras-ing branches to ensure the integrality, which can force the network to select other complementary snippets by erasing the most discriminative snippets. It is worth mentioning that a ternary mask is utilized to provide erasing branches with a background prior to enhance the separability of the model. Besides, we design a background suppression branch to further reduce the effect of background snippets. Extensive experiments on dataset THUMOS'14 and ActivityNet show the effectiveness of our method. (c) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Weak supervision
Temporal action localization
Erasing branches
Ternary mask
Background suppression branch
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
Learning principal orientations and residual descriptor for action recognition.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveLearning motion representation for real-time spatio-temporal action localizationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSave
ShareSaveA survey on activity recognition and behavior understanding in video surveillanceVISUAL COMPUTER
VISUAL COMPUTERIF2.9
PREAI
ShareSaveShareSaveLong-Term Effects of Cathodic Protection on Prestressed Concrete Structures: Hydrogen Embrittlement of Prestressing SteelCORROSION
CORROSIONIF0
PREAI
ShareSaveShareSave
View more